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At least 1,459 records · Page 81

Calibration verification for stochastic agent-based disease spread models

Accurate disease spread modeling is crucial for identifying the severity of outbreaks and planning effective mitigation efforts. To be reliable when applied to new outbreaks, model calibration techniques must be robust. However, current methods frequently forgo calibration verification (a stand-alone process evaluating the calibration procedure) and instead use overall model validation (a process comparing calibrated model results to data) to check calibration processes, which may conceal errors in calibration. In this work, we develop a stochastic agent-based disease spread model to act as a testing environment as we test two calibration methods using simulation-based calibration, which is a synthetic data calibration verification method. The first calibration method is a Bayesian inference approach using an empirically-constructed likelihood and Markov chain Monte Carlo (MCMC) sampling, while the second method is a likelihood-free approach using approximate Bayesian computation (ABC). Simulation-based calibration suggests that there are challenges with the empirical likelihood calculation used in the first calibration method in this context. These issues are alleviated in the ABC approach. Despite these challenges, we note that the first calibration method performs well in a synthetic data model validation test similar to those common in disease spread modeling literature. We conclude that stand-alone calibration verification using synthetic data may benefit epidemiological researchers in identifying model calibration challenges that may be difficult to identify with other commonly used model validation techniques.

60 APPLIED LIFE SCIENCES↗

A scalable variational method for estimating the latent infection-rate field of an outbreak

In this paper, we explore whether the infection-rate of a disease can serve as a robust monitoring variable in epidemiological surveillance algorithms. The infection-rate is dependent on population mixing patterns that do not vary erratically day-to-day; in contrast, daily case-counts used in contemporary surveillance algorithms are corrupted by reporting errors. The technical challenge lies in estimating the latent infection-rate from case-counts. Here we devise a Bayesian method to estimate the infection-rate across multiple adjoining areal units, and then use it, via an anomaly detector, to discern a change in epidemiological dynamics. We extend an existing model for estimating the infection-rate in an areal unit by incorporating a Markov random field model, so that we may estimate infection-rates across multiple areal units, while preserving spatial correlations observed in the epidemiological dynamics. To carry out the high-dimensional Bayesian inverse problem, we develop an implementation of mean-field variational inference specific to the infection model and integrate it with the random field model to incorporate correlations across counties. The method is tested on estimating the COVID-19 infection-rates across all 33 counties in New Mexico using data from the summer of 2020, and then employing them to detect the arrival of the Fall 2020 COVID-19 wave. We perform the detection using a temporal algorithm that is applied county-by-county. We also show how the infection-rate field can be used to cluster counties with similar epidemiological dynamics.

60 APPLIED LIFE SCIENCES↗

Adaptive linear predictor FIR filter based on the Cyclone V FPGA with HPS to reduce narrow band RFI in AERA radio detection of cosmic rays

We present the new approach to a filtering of radio frequency interferences (RFI) in the Auger Engineering Radio Array (AERA) which study the electromagnetic part of the Extensive Air Showers. The radio stations can observe radio signals caused by coherent emissions due to geomagnetic radiation and charge excess processes. AERA observes frequency band from 30 to 80 MHz. This range is highly contaminated by human-made RFI. In order to improve the signal to noise ratio RFI filters are used in AERA to suppress this contamination. The first kind of filter used by AERA was the Median one, based on the Fast Fourier Transform (FFT) technique. The second one, which is currently in use, is the infinite impulse response (IIR) notch filter. The proposed new filter is a finite impulse response (FIR) filter based on a linear prediction (LP). A periodic contamination hidden in a registered signal (digitized in the ADC) can be extracted and next subtracted to make signal cleaner. The FIR filter requires a calculation of n=32, 64 or even 128 coefficients (dependent on a required speed or accuracy) by solving of n linear equations with coefficients built from the covariance Toeplitz matrix. This matrix can be solved by the Levinson recursion, which is much faster than the Gauss procedure. The filter has been already tested in the real AERA radio stations on Argentinean pampas with a very successful results. The linear equations were solved either in the virtual soft-core NIOSR processor (implemented in the FPGA chip as a net of logic elements) or in the external Voipac PXA270M ARM processor. The NIOS processor is relatively slow (50 MHz internal clock), calculations performed in an external processor consume a significant amount of time for data exchange between the FPGA and the processor. Test showed a very good efficiency of the RFI suppression for stationary (long-term) contaminations. However, we observed a short-time contaminations, which could not be suppressed either by the IIR-notch filter or by the FIR filter based on the linear predictions. For the LP FIR filter the refreshment time of the filter coefficients was to long and filter did not keep up with the changes of a contamination structure, mainly due to a long calculation time in a slow processors. We propose to use the Cyclone V SE chip with embedded micro-controller operating with 925 MHz internal clock to significantly reduce a refreshment time of the FIR coefficients. The lab results are promising. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Holistic Fuel Cell Electric Vehicle/Hydrogen Station Optimization Model (CRADA CRD-18-00745 Final Report)

The National Renewable Energy Lab (NREL), Argonne National Lab (ANL), Sandia National Lab (SNL), and Frontier Energy, Inc comprise a team that will perform a review of currently available models that can simulate both a hydrogen station and a fuel cell electric vehicle (FCEV), and whose owners are willing to make them available freely. We will then down select to a single model or model pair, secure agreements for their free use, and validate the resulting model or coupled models. For example, we may evaluate an existing 1-D hydrogen fueling model developed by Kyushu University for the Japanese New Energy and Industrial Technology Development Organization (NEDO). The validation will take place using data from various sources, potentially including the European Joint Research Center (JRC) HyTransfer project, SAE testing performed by Powertech to validate SAE J2601, and whole-station validation using the NREL’s Hydrogen Infrastructure Testing and Research Facility (HITRF). The team will make the model and all the validation data open to the public at the end of the project.

1-D hydrogen fueling model↗

Analyzing the Effects of Atmospheric Turbulent Fluctuations on the Wake Structure of Wind Turbines and Their Blade Vibrational Dynamics

In recent trends, a rising demand for renewable energy has driven wind turbines to larger proportions, where lighter blade designs are often adopted to reduce the costs associated with logistics and production. This causes modern utility-scale wind turbine blades to be inherently more flexible, and their amplified aeroelastic sensitivity results in complex multi-physics reactions to variant atmospheric conditions, including dynamic patterns of aerodynamic loading at the rotor and vortex structure evolutions within the wake. In this paper, we analyze the influence of inflow variance for wind turbines with large, flexible rotors through simulations of the National Rotor Testbed (NRT) turbine, located at Sandia National Labs’ Scaled Wind Farm Technology (SWiFT) facility in Lubbock, Texas. The Common Ordinary Differential Equation Framework (CODEF) modeling suite is used to simulate wind turbine aeroelastic oscillatory behavior and wind farm vortex wake interactions for a range of flexible NRT blade variations, operating in differing conditions of variant atmospheric flow. CODEF solutions of turbine operation in Steady-In-The-Average (SITA) wind conditions are compared to SITA wind conditions featuring a controlled gust-like pulse overimposed, to isolate the effects of typical wind fluctuations. Finally, simulations of realistic time-varying wind conditions from SWiFT meteorological tower measurements are compared to the solutions of SITA wind conditions. These increasingly complex atmospheric inflow variations are tested to show the differing effects evoked by various patterns of spatiotemporal atmospheric flow fluctuations. An analysis is presented for solutions of wind turbine aeroelastic response and vortex wake evolution, to elucidate the consequences of variant inflow, which pertain to wind turbine dynamics at an individual and farm-collective scale. The comparisons of simulated farm flow for SITA and measured fluctuating wind conditions show that certain regions of the wake contain up to a 12% difference in normalized axial velocity, due to the introduction of wind fluctuations. The findings of this study prove valuable for practical applications in wind farm control and optimization strategies, with particular significance for modern utility-scale wind power plants operating in variant atmospheric conditions.

Farrell, Alayna (ORCID:000000023555720X)↗

Crystalline Organic Compounds as Effective Nuclei in Contact Efflorescence of Ammonium Sulfate

Contact nucleation is believed to play a role in liquid-to-solid phase transitions in the atmosphere including ice nucleation and salt efflorescence. Here, for this work, contact efflorescence of optically levitated ammonium sulfate droplets by collisions with organic particles is probed using a long working-distance optical trap. Two highly viscous water-soluble organic compounds (d-(+)-raffinose and citric acid), and two insoluble highly surface-active organic compounds (stearic acid and cis-pinonic acid) were probed for their ability to induce efflorescence upon contact. While three of the organics showed minimal effectiveness as contact nuclei, cis-pinonic acid showed a remarkable ability to initiate contact efflorescence of ammonium sulfate, occurring near ammonium sulfate’s deliquescence relative humidity. Further analysis of cis-pinonic acid using bright-field microscopy in an electrodynamic balance provided evidence that the cis-pinonic acid particles are crystalline under the laboratory conditions. We suggest that the close lattice match between crystalline ammonium sulfate and crystalline cis-pinonic acid may account for the observed effectiveness in initiating contact efflorescence of ammonium sulfate. In contrast, tests of contact nucleation between cis-pinonic acid and sodium chloride, a pair with a poor lattice match, did not result in efflorescence. These findings suggest that crystalline organic compounds in the atmosphere could act as effective nuclei for contact efflorescence of atmospherically relevant salts, provided they share a compatible lattice structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Deep Learning-enhanced Block-Diagram Modeling of Solar Power Systems

Data-driven models of power system inverter-based resources are desired to run simulations faster than with detailed electromagnetic transient models, to hide proprietary design details, to support control system design applications, and to aggregate the effects of distributed energy resources. This paper applies a customized Hammerstein Wiener framework to train block diagram models from thousands of electromagnetic transient simulations or experimental test records. The block diagram models integrate with larger grid simulations as voltagecontrolled current sources or current-controlled voltage sources for several simulators. Guidelines for block architecture and training are presented. Three-phase balanced, three-phase unbalanced, and single-phase examples all achieve an acceptable root mean square error of no more than 0.05 per-unit.

Mcdermott, Thomas E. [Private consulting company]↗

Genetics and Genomics of Pathogen Resistance in Switchgrass (Final Report)

This project was funded by DOE under Grant no. DE-SC0016108. Originally approved for the 2016-2019 period, two no-cost extensions were solicited and approved, which prolonged the lifespan through July 2021. This final report informs on the results obtained so far from the research implemented. The research hinged on integrating genomics (genomic selection, RNAseq, virus-plant interactions) with classical genetics (conventional breeding) to incorporate durable resistance to fungal (rust) and viral (mosaic) diseases in switchgrass (Panicum virgatum) populations being bred for bioenergy. Higher biomass yield, higher quality (low lignin content), and durable disease resistance are key features to make lignocellulosic switchgrass feedstocks economically competitive and sustainable. Genomic selection is being applied on three generations of a switchgrass population derived from crossing two ecotypes (Kanlow as lowland female and Summer as upland male) with differential performance in terms of biomass yield and quality, disease resistance, and winter survivability. Target populations were screened for rust and mosaic in field and/or lab and phenotyped for biomass yield and quality traits. Genetic analyses were applied across generations to capture the joint inheritance of the targeted traits and predict breeding values for parents and progeny with greater accuracy. Parental and a panel of different switchgrass populations were genotyped with the DArTseq technology to develop SNP (0, 1, 2) and in-silico (presence/absence) DArT markers. Rust inoculations techniques were developed and applied successfully on switchgrass. The original populations (Kanlow and Summer) were sequenced with RNAseq to capture the gene expression profiles across sequential time-points and appraise the basis of greater resistance in the Kanlow vs the Summer ecotype. Constructs of PMV and sPMV mosaic virus were assembled and tested first on proso millet to find the best protocol to use later on switchgrass. Results from the preliminary analyses indicate that 1) ample additive genetic variation is available for selection and improving this inter-ecotypic population for yield, quality, and disease traits, 2) significant gains are to be expected with the genetic correlations being favorable between yield and lignin content and between yield and disease ratings, 3) substantial differences exist in the genetic regions controlling rust resistance in the two ecotypes, 4) co-infection with PMV isolates from Nebraska and its satellite from Kansas elicit severe mosaic symptoms, and 5) two different genetic systems are responsible for imparting resistance to rust and virus in switchgrass.

59 BASIC BIOLOGICAL SCIENCES↗

Investigation of Rare Earth Element Extraction from North Dakota Coal-Related Feedstocks (Phase 2 Final Technical Report)

There were four major goals of this project: i) identify promising lignite or lignite-related feedstocks with total rare earth element content above 300 parts per million (ppm), ii) evaluate at the lab and bench-scale the extractability of the REE from the material, iii) determine the processing required to generate REE concentrates in excess of 2 wt%, iv) evaluate the economic feasibility of the process and develop a pathway for commercialization. In the Phase 1 study, work was primarily focused upon resource evaluation and labscale extractability of the REE, with a preliminary techno-economic assessment (TEA) conducted, whereas in the Phase 2 work, the extraction process was conducted at the bench scale, a method for cost-effective generation of higher purity mixed REE concentrates (up to 65%) was developed, and a refined TEA on the bench-scale data for a 1,000 kg coal processing test was conducted.

01 COAL, LIGNITE, AND PEAT↗

Microstructure Clones

Background: A material’s microstructure drives its material performance. Contemporary crystal plasticity experiments compare full-field strain measurements of polycrystal specimens to models. Because each specimen is unique, it is impossible to know which features of the observed deformation are deterministic vs statistical; thus, differences between model and experiment may or may not be significant. Objective: This paper introduces the invention of microstructure clones. Microstructure clones are 2D oligocrystal specimens that have nearly identical microstructures to remedy the aforementioned experimental limitations. Having specimens with nearly identical microstructures will allow for multiple destructive tests of a microstructure (either as repeats or intentionally different experiments), an ability to “see the future” by providing insight into how a specimen will deform, variability quantification, and experimental investigations of response to small microstructural changes. Methods: This work introduces microstructure clones. Repeatability of these clones is demonstrated in tensile bars of pure nickel. Local strain measurements from digital image correlation are compared between clone specimens and compared to results from a crystal plasticity finite element model. Results: Two sets of microstructure clones were tested in this study and displayed very consistent deformation responses within each clone set. Small observed differences in deformation invite investigation into microstructure stochasticity and the effect of small microstructural and loading differences. Conclusions: Microstructure clones represent a significant shift in understanding structure–property relationships. This work reshapes experimental crystal plasticity to allow for experiments that control for specific variables, quantification of microstructural stochasticity (and other sources of stochasticity), and opportunities for replicating experiments.

Crystal Plasticity↗

Final Report of Tank 241-C-105 Dissolution, the Phase 2 Study

Three clamshell grab samples were taken from Tank 241-C-105 in October 2015 in accordance with RPP-PLAN-60011. Analytical results of those samples were issued in the report RPP-RPT-59115 by Wastren Advantage, Inc., Hanford Laboratory. Solid phase characterization results were reported separately in LAB-RPT-15-00011 and in RPP-RPT-59147. The major solid phases reported to be present were dawsonite [NaAlCO 3 (OH) 2 ], trona [Na 3 (HCO 3 )(CO 3 )·2H 2 O], cejkaite [Na 4 (UO 2 )(CO 3 ) 3 ], and an unidentified organic solid, with minor amounts of gibbsite [Al(OH) 3 ], natrophosphate [Na 7 F(PO 4 ) 2 ·19H 2 O], and traces of unidentified iron-rich and manganese-rich phases. Note that the presence of dawsonite, trona, and cejkaite requires a relatively low pH, likely around pH 9 to 10. One aliquot of each grab sample was provided to 222-S Laboratory Process Chemistry for dissolution studies. Phase 1 of the dissolution testing followed the approved test plan, WRPS-1404813, Rev. 3, and examined the behavior of the Tank 241-C-105 solids treated with water, 19M sodium hydroxide, 2M nitric acid, and 0.5M oxalic acid/2M nitric acid. Phase 2 of the testing was conducted in accordance with instructions from the client and emphasized treatment with 19M sodium hydroxide followed by water washing. This is the report of the Phase 2 testing.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

PNNL-Predictive-Phenomics/ProteoMeter

ProteoMeter is a Python package that assists in the statistical analysis of global proteomics, protein post-translation modification (PTM), and limited proteolysis (LiP) data. It contains batch correction, normalization, and statistical testing methods, as well as functions that "roll up" peptide-level data to the single-site level. It has a robust user configuration system, allowing it to flexibly integrate different types of experiment designs. For basic usage, a simple configuration file provides the essential functionality. Advanced users have access to the entire statistical pipeline for fine-tuning analyses. Processed data is easily exported to many common spreadsheet and data-frame formats.

Rozum, Jordan [Pacific Northwest National Lab]↗

Highly Selective and Stable Multivariable Gas Sensors for Enhanced Robustness and Reliability of SOFC Operation

GE Global Research, in partnership with SUNY Polytechnic Institute (SUNY Poly) and GE–Fuel Cells LLC, designed, built, and tested multi-gas sensor prototypes for in situ monitoring of gases produced during steam reforming in solid oxide fuel cell (SOFC) systems. To be widely adopted and to deliver a lower operating cost for customers, SOFC systems should enhance their operation reliability and improve stack robustness. These needs can be achieved by early diagnostics of potential upsets by using in-situ gas sensors for the real-time monitoring of gas mixture compositions. Thus, this project objective was to achieve selectivity and stability of sensing of gases for SOFC applications by implementing a new generation of gas sensors, known as multivariable sensors. Such sensors are intended to complement the mature traditional analytical instruments based on sensor performance and design characteristics. The key sensor-performance characteristics include accurate detection and quantitation of several gases in their mixtures and stability of operation in unattended applications. The key design characteristics of sensors include their unobtrusive form factor and cost-effectiveness. The duration of the project was 18 months with the project structure that included three technical tasks such as (1) design and laboratory validation of multivariable sensors for selective detection of H 2 and CO gases, (2) laboratory validation of sensor stability over a two-week testing, and (3) field-validation of the developed multivariable sensor system prototype at GE–Fuel Cells factory. Multi-gas detection was achieved with multivariable photonic sensors designed to operate at elevated temperature. The sensor design was based on three-dimensional (3D) bio-inspired photonic nanostructure that was fabricated using materials responsive to gases of interest such as H 2 and CO. The gas discrimination and the rejection of interferences such as CH 4 , H 2 O, and others was performed by optimizing the design parameters of the 3D photonic nanostructures such as their composition and morphology. Planar sol-gel and sputtered sensing films were fabricated as controls. As a result of the fabrication, functionalization, excitation, and performance testing of 3D photonic sensors, we developed initial design rules for high temperature multivariable sensors. These design rules pave the way for the new generation of cost-effective industrial sensors. Stability of sensor operation was tested in the laboratory conditions under variations of the ambient environments. Stability tests were designed in the order of enhanced complexity and realism of sensor operation and included quantitation of a single gas, quantitation of a single gas in the presence of interferences, and quantitation of two and three gases. We have found that conventional machine learning data analysis stools were unable to correct for the sensor drift. Thus, we have developed new methods of machine learning data analysis that provided us with needed performance stability. Such performance improvement in gas sensing was a significant milestone in bringing our multivariable sensors to practical applications. The project culminated with initial field validation of developed multivariable sensor prototypes at GE–Fuel Cells LLC. The in situ data generated by the sensor system in the lab and field conditions provided inputs that advanced our fundamental understanding of gas sensing at high temperatures to enable sensors for SOFC applications and allowed development of the recommendations for further deliverables.

47 OTHER INSTRUMENTATION↗

Multi-scale dynamic modeling and validation of radial flow fixed bed contactors for post-combustion CO 2 capture using bench scale and pilot plant data

Here, in this work, a multi-scale model of a radial flow fixed bed contactor packed with a carbon sorbent is developed and validated with laboratory-scale and pilot plant scale dynamic data. For the lab scale system, the model results were compared with low, medium and high gas and sweep flowrates, yielding root mean square error (RMSE) of 0.80, 0.63, 0.96 CO 2 mol%, respectively, for the outlet CO 2 concentration profile considering the entire A-D cycle. For the bed outer temperature profile, maximum RMSE was found to be 5.5 °C considering all flowrates and entire A-D cycles. An experimental campaign was developed and applied to a pilot plant at Technology Center Mongstad (TCM), Norway. Approaches were developed for pre-processing of data including consideration of the effect of gas mixing, measurement delay, and determination of cyclic steady-state conditions. Considering profiles during A-D cycles for all test runs, it was found that the maximum RMSE for pressure drop, temperature for the outer section of the bed, temperature for the middle section of the bed, and outlet CO 2 concentration profile remained less than 1.5 mbar, 3.5 °C, 2.8 °C, and 1.3 CO 2 mol%, respectively. The validated model was used to perform sensitivity studies on several key design operating variables for the adsorption-desorption cycle. It was found that the flow rate and concentration of flue gas have dominant nonlinear effects on the breakthrough time while the desorption time was strongly affected by the sweep gas flowrate for the specific sorbent being evaluated in this study.

20 FOSSIL-FUELED POWER PLANTS↗

PVEMC: isolating the flavor-dependent EMC effect using parity-violating inelastic scattering in SoLID

In order to better understand the EMC effect, we propose a clean and precise measurement of the flavor dependence of the EMC effect using parity-violating deep inelastic scattering on a 48 Ca target. This measurement will provide an extremely sensitive test for flavor dependence in the modification of nuclear parton distribution functions (PDFs) for neutron-rich nuclei. A measurement of the flavor dependence will provide new and vital information and help to explain nucleon modification at the quark level. In addition to helping understand the origin of the EMC effect, a flavor-dependent nuclear pdf modification could significantly impact a range of processes, including neutrino-nucleus scattering, nuclear Drell-Yan processes, and e-A observables at the Electron-Ion Collider. The parity-violating asymmetry A PV from 48 Ca using an 11 GeV beam at 80 μA will be measured using the SoLID detector, proposed for a series of measurements in Hall A at Jefferson Lab. In 68 days of data taking, we will reach 0.7–1.3% statistical precision for 0.2 < x < 0.7 with 0.6–0.7 systematic uncertainties. Here, the goal is to make the first direct measurement of the flavor dependence of the EMC effect. The precision of the measurement will allow for quantification of the flavor-dependent effects, greatly improving our ability to differentiate between models of the EMC effect and constraining the u- and d-quark contributions in neutron rich nuclei.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

SQUID Noise Measurements for CDMS Detectors

The Cryogenic Dark Matter Search is the second iteration of SuperCDMS dark matter experiments with an increase in sensitivity to low mass dark matter particles. The experiment uses germanium and silicon crystals which are capable of detecting both the charges and phonon signals produced from dark matter interactions. Detectors are housed in towers which are then housed in a cryogenics system able to cool down to 15 mK. The experiment will be installed in an underground lab in Sudbury, Ontario in order to shield from cosmic rays and background radiation. SQUIDs are an acronym for superconducting quantum interference devices that are capable of detecting extremely small magnetic fields. A typical dark matter nuclear recoil interaction can be detected by a TES (transition edge sensor). SQUIDs are then used to read out and amplify the signals generated by the TES. It is essential that these SQUIDS add negligible noise to the intrinsic noise of the TES to be able to distinguish the internal circuit noise from a true dark matter interaction signal. The noise was measured while varying several parameters. Both an Agilent and an SRS785 spectrum analyzer were used. Furthermore, an amplifier which amplified the signal 10x was tested. The Agilent machine produced more noticeably greater noise in the region from 100-1000 Hz, whereas the SRS785 did not. Next, the amplifier was tested and the signal generated was analyzed in comparison to the original signal. The amplifier did not produce any noticeable additional noise over the intrinsic noise of the SQUID. Further work includes testing to see if detectors with higher voltage sources can also be used with negligible noise.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Completion Report for Well ER-20-12: Corrective Action Units 101 and 102: Central and Western Pahute Mesa

Well ER-20-12 was drilled for the U.S. Department of Energy, Nevada National Security Administration Nevada Field Office in support of the Underground Test Area Activity. The well was drilled from October 2015 to January 2016 as an addition to the Central and Western Pahute Mesa corrective action units 101 and 102 the Phase II drilling program. Well ER-20-12 was identified based on recommendations of the Pahute Mesa Guidance Team as a result of anomalous tritium detections in groundwater samples collected from Well PM-3 in 2011 and 2013. The primary purpose of the well was to provide information on the hydrogeology in the area downgradient of select underground tests on Western Pahute Mesa and define hydraulic properties in the saturated Tertiary volcanic rocks. The main 46.99-centimeter (cm) (18.5-inch [in.]) borehole was drilled to a depth of 765.14 meters (m) (2,510.3 ft) and the hole opened to 66.04 cm (26 in.); followed by the 50.80-cm (20-in.) surface casing, which was installed and sealed with cement; and a piezometer (p4) was set in the Timber Mountain welded-tuff aquifer (TMWTA) between the casing and the open borehole. The borehole was continued with a 46.99-cm (18.5-in.) drill bit to a depth of 1,326.53 m (4,352.16 ft), and an intermediate 24.44-cm (9.625-in.) casing was installed and sealed to 1,188.72 m (3,900.00 ft) A piezometer (p3) was installed across the Calico Hills zeolitic composite unit (CHZCM) (lava-flow aquifer [LFA]) in the annulus of the open borehole. Two additional piezometers were installed and completed between the intermediate casing and the borehole wall, one (p2) in the CHZCM and one (p1) in the Belted Range aquifer (BRA). The piezometers are set to monitor groundwater properties in the completed intervals. The borehole was continued with a 21.59-cm (8.5-in.) drill bit to a total depth of 1,384.80 m (4,543.33 ft), and the main completion 13.97-cm (5.5-in.) casing was installed in the open borehole across the Pre-Belted Range composite unit (PBRCM). Data collected during hole construction include composite drill cutting samples collected every 3.0 m (10 ft), geophysical logs, hydrophysical logs, percussion core samples, water-quality measurements (including tritium), and water-level measurements. The well penetrated 1,384.4 m (4,543.33 ft) of Tertiary volcanic rocks. The stratigraphy and lithology were generally as expected with one noted exception. A thick lava-flow and related ash-flow tuffs were identified as Calico Hills Formation (Th), and no Crater Flat units were noted. Additionally, many of the Thirsty Canyon and Timber Mountain units were thicker than expected. Fluid levels measured in the borehole during drilling are the following: (1) on November 2, 2015, Navarro measured the fluid level in the borehole at a depth of 492.33 m (1,615.25 ft) below ground surface (bgs); (2) Schlumberger and COLOG recorded fluid levels during geophysical logging on November 4 and 5, 2015, at a depth of 492.86 m (1,617 ft) and 492.25 m (1,615 ft) bgs, respectively; and (3) on December 4, 2015, COLOG and Navarro measured fluid level in the 20-in. casing with an open borehole to 1,326.54 m (4,352.16 ft) bgs at 575.77 m (1,889.00 ft) and 574.03 m (1,883.3 ft) bgs, respectively. These and subsequent water-level measurements indicate a potential head difference of greater than 76.2 m (250 ft) for groundwater in aquifers above and below the Upper Paintbrush confining unit (UPCU). As expected, tritium was occasionally measured above the Safe Drinking Water Act limit (20,000 picocuries per liter [pCi/L]). Lab analysis on four bailed samples and taken from the undeveloped well indicate that the tritium activities average approximately 36,545 pCi/L. All Fluid Management Plan (FMP) requirements for Well ER-20-12 were met. Analysis of monitoring samples and FMP confirmatory samples indicate that fluids generated during drilling at ER-20-12 met the FMP criteria for discharge to the lined sump and designated infiltration area. All sanitary and hydrocarbon waste generated was properly handled and disposed of.

54 ENVIRONMENTAL SCIENCES↗

MTRE

Multi-Token Reliability Estimation (MTRE) is a lightweight, white-box hallucination detector for vision-language models. Instead of using only the first output token, MTRE aggregates logits from the first ~10 tokens and feeds them to a small attention-based reliability head; per-token scores are combined via a sequential log-likelihood-ratio test with early-stopping, and an MTRE-t variant calibrates thresholds via cross-fitting. MTRE reports average gains of +9.4% Accuracy and +14.8% AUROC over common baselines across MAD-Bench, MM-SafetyBench, MathVista, and arithmetic/counting tasks, while adding ~4.3M params and ~1% inference overhead (~26 MB VRAM, ~0.94 ms per detection). Key limitation: requires access to early token logits and is evaluated on a handful of open-source 7B VLMs.

Bhattarai, Manish [Los Alamos National Labs]↗