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NASA NTRS2015

Forest Disturbance Across the Conterminous United States from 1985-2012: The Emerging Dominance of Forest Decline

Evidence of shifting dominance among major forest disturbance agent classes regionally to globally has been emerging in the literature. For example, climate-related stress and secondary stressors on forests (e.g., insect and disease, fire) have dramatically increased since the turn of the century globally, while harvest rates in the western US and elsewhere have declined. For shifts to be quantified, accurate historical forest disturbance estimates are required as a baseline for examining current trends. We report annual disturbance rates (with uncertainties) in the aggregate and by major change causal agent class for the conterminous US and five geographic subregions between 1985 and 2012. Results are based on human interpretations of Landsat time series from a probability sample of 7200 plots (30 m) distributed throughout the study area. Forest disturbance information was recorded with a Landsat time series visualization and data collection tool that incorporates ancillary high-resolution data. National rates of disturbance varied between 1.5% and 4.5% of forest area per year, with trends being strongly affected by shifting dominance among specific disturbance agent influences at the regional scale. Throughout the time series, national harvest disturbance rates varied between one and two percent, and were largely a function of harvest in the more heavily forested regions of the US (Mountain West, Northeast, and Southeast). During the first part of the time series, national disturbance rates largely reflected trends in harvest disturbance. Beginning in the mid-90s, forest decline-related disturbances associated with diminishing forest health (e.g., physiological stress leading to tree canopy cover loss, increases in tree mortality above background levels), especially in the Mountain West and Lowland West regions of the US, increased dramatically. Consequently, national disturbance rates greatly increased by 2000, and remained high for much of the decade. Decline-related disturbance rates reached as high as 8% per year in the western regions during the early-2000s. Although low compared to harvest and decline, fire disturbance rates also increased in the early- to mid-2000s. We segmented annual decline-related disturbance rates to distinguish between newly impacted areas and areas undergoing gradual but consistent decline over multiple years. We also translated Landsat reflectance change into tree canopy cover change information for greater relevance to ecosystem modelers and forest managers, who can derive better understanding of forest-climate interactions and better adapt management strategies to changing climate regimes. Similar studies could be carried out for other countries where there are sufficient Landsat data and historic temporal snapshots of high-resolution imagery.

Cohen, Warren B.↗

DOE OSTI2026

Uncertainty guided online ensemble for non-stationary data streams in fusion science

Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior with distribution drifts, resulted by both experimental evolution and machine wear-and-tear. ML models assume stationary distribution and fail to maintain performance when encountered with such non-stationary data streams. Online learning techniques have been leveraged in other domains, however it has been largely unexplored for fusion applications. In this paper, we investigate online learning for continuous adaptation to drifting data streams in the prediction of Toroidal Field (TF) coils deflection at the DIII-D fusion facility. We further address the short-term performance degradation inherent to standard online learning, which arises because ground truth is unavailable at prediction time. To mitigate this issue, we propose an uncertainty-guided online ensemble framework. The method leverages the Deep Gaussian Process Approximation (DGPA) for calibrated uncertainty estimation and uses these uncertainty measures to guide a meta-algorithm that aggregates predictions from learners trained over different historical horizons. Our results show that online learning reduces prediction error by 80% compared to a static model. The online ensemble and the proposed uncertainty-guided ensemble further reduce error by approximately 6%, and 10% respectively, relative to standard single-model online learning, while also providing calibrated uncertainty estimates to support operational decision-making.

AI↗

DOE OSTI2021

Conservation management decreases surface runoff and soil erosion

Conservation management practices – including agroforestry, cover cropping, no-till, reduced tillage, and residue return – have been applied for decades to control surface runoff and soil erosion, yet results have not been integrated and evaluated across cropping systems. In this study we collected data comparing agricultural production with and without conservation management strategies. We used a bootstrap resampling analysis to explore interactions between practice type, soil texture, surface runoff, and soil erosion. We then used a correlation analysis to relate changes in surface runoff and soil erosion to 13 other soil health and agronomic indicators, including soil organic carbon, soil aggregation, infiltration, porosity, subsurface leaching, and cash crop yield. Across all conservation management practices, surface runoff and erosion had respective mean decreases of 67% and 80% compared with controls. Use of cover cropping provided the largest decreases in erosion and surface runoff, thus emphasizing the importance of maintaining continuous vegetative cover on soils. Coarse- and medium-textured soils had greater decreases in both erosion and runoff than fine-textured soils. Changes in surface runoff and soil erosion under conservation management were highly correlated with soil organic carbon, aggregation, porosity, infiltration, leaching, and yield, showing that conservation practices help drive important interactions between these different facets of soil health. This study offers the first large-scale comparison of how different conservation agriculture practices reduce surface runoff and soil erosion, and at the same time provides new insight into how these interactions influence the improvement or loss of soil health.

58 GEOSCIENCES↗

DOE OSTI2023

Short-lead seasonal precipitation forecast in northeastern Brazil using an ensemble of artificial neural networks

This study assesses the deterministic and probabilistic forecasting skill of a 1-month-lead ensemble of Artificial Neural Networks (EANN) based on low-frequency climate oscillation indices. The predictand is the February-April (FMA) rainfall in the Brazilian state of Ceará, which is a prominent subject in climate forecasting studies due to its high seasonal predictability. Additionally, the study proposes combining the EANN with dynamical models into a hybrid multi-model ensemble (MME). The forecast verification is carried out through a leave-one-out cross-validation based on 40 years of data. The EANN forecasting skill is compared with traditional statistical models and the dynamical models that compose Ceará’s operational seasonal forecasting system. A spatial comparison showed that the EANN was among the models with the smallest Root Mean Squared Error (RMSE) and Ranked Probability Score (RPS) in most regions. Moreover, the analysis of the area-aggregated reliability showed that the EANN is better calibrated than the individual dynamical models and has better resolution than Multinomial Logistic Regression for above-normal (AN) and below-normal (BN) categories. It is also shown that combining the EANN and dynamical models into a hybrid MME reduces the overconfidence of the extreme categories observed in a dynamically-based MME, improving the reliability of the forecasting system.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Finely Dispersed Micro- and Nano-Gold Recovery Using Thermomorphic Polymer with Diphenylphosphine

The article reports test data on adsorption and flotation characteristics of thermomorphic polymer joined with diphenylphosphine—TMPD. The test materials are natural pyrite and arsenopyrite, and models of gold-bearing sulfide minerals. Electron microscopy and X-ray structure analysis prove that reductive adsorption of gold at the surface of sulfide minerals from HAuCl{sub 4} solution allows modeling natural aggregates of sulfides and finely dispersed gold. UV spectrophotometry proves selective adsorption of TMPD at gold. Laser scanning microscopy shows connection of TMPD with micro- and nano-size gold on polished sections of pyrite and arsenopyrite. During monomineral flotation, joint use of TMPD and butyl xanthate at a ratio of 1:1 doubles the yield of gold-bearing sulfides and enables maximum recovery of gold minerals at the total consumption of reagents reduced by 30–50%. It is found that TMPD is selective relative to gold-bearing minerals and its combination with xanthate ensures a considerable increment in gold-bearing mineral recovery by flotation.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2023

FrESCO

The National Cancer Institute (NCI) monitors population level cancer trends as part of its Surveillance, Epidemiology, and End Results (SEER) program. This program consists of state or regional level cancer registries which collect, analyze, and annotate cancer pathology reports. From these annotated pathology reports, each individual registry aggregates cancer phenotype information and summary statistics about cancer prevalence to facilitate population level monitoring of cancer incidence. Extracting cancer phenotype from these reports is a labor intensive task, requiring specialized knowledge about the reports and cancer. Automating this information extraction process from cancer pathology reports has the potential to improve not only the quality of the data by extracting information in a consistent manner across registries, but to improve the quality of patient outcomes by reducing the time to assimilate new data and enabling time-sensitive applications such as precision medicine. Here we present FrESCO: Framework for Exploring Scalable Computational Oncology, a modular deep-learning natural language processing (NLP) library for extracting pathology information from clinical text documents.

Spannaus, Adam [Oak Ridge National Lab. (ORNL), Oa↗

NASA NTRS1976

LANDSAT follow-on: A report by the applications survey groups. Volume 1: Executive summary

Attempts at operational usage of the LANDSAT imagery by non NASA users are studied with particular emphasis on profitable use of the imagery, as contrasted with investigations concerned with research and development of a technology. An evaluation is given of the functional capabilities of the LANDSAT follow-on and ground systems designs in terms of user requirements and desiderata for data measurements, products, and parameters. Applications survey groups (ASGs) were formed for mineral and petroleum exploration, inland water resources, land inventory, and agriculture. The members were drawn from all segments of the user community: Federal agencies, state and local governments or agencies (or from associations of such constituencies), industry and universities. They were selected so that in aggregate they would be able to adequately assess the state-of-the-art in their technical areas and represent this in the ASG deliberations.

Billingsley, F. C.↗

NASA NTRS2013

Chemical Heterogeneity of a Large Cluster IDP: Clues to its Formation History Using X-ray Fluorescence Mapping and XANES Spectroscopy

Chondritic porous IDPs may be among the most primitive objects found in our solar system [1]. They consist of many micron to submicron minerals, glasses and carbonaceous matter [2,3,4,5,6,7] with > 10(exp 4) grains in a 10 micron cluster [8]. Speculation on the environment where these fine grained, porous IDPs formed varies with possible sources being presolar dusty plasma clouds, protostellar condensation, solar asteroids or comets [4,6,9]. Also, fine grained dust forms in our solar system today [10,11]. Isotopic anomalies in some particles in IDPs suggest an interstellar source[4,7,12]. IDPs contain relic particles left from the dusty plasma that existed before the protostellar disk formed and other grains in the IDPs formed later after the cold dense nebula cloud collapsed to form our protostar and other grains formed more recently. Fe and CR XANES spectroscopy is used here to investigate the oxygen environment in a large (>50 10 micron or larger sub-units) IDP. Conclusions: Analyzing large (>50 10 micron or larger sub-units) CP IDPs gives one a view on the environments where these fine dust grains formed which is different from that found by only analyzing the small, 10 micron IDPs. As with cluster IDP L2008#5 [3], L2009R2 cluster #13 appears to be an aggregate of grains that sample a diversity of solar and perhaps presolar environments. Sub-micron, grain by grain measurement of trace element contents and elemental oxidation states determined by XANES spectroscopy offers the possibility of understanding the environments in which these grains formed when compared to standard spectra. By comparing thermodynamic modeling of condensates with analytical data an understanding of transport mechanisms operating in the early solar system may be attained.

Wirick, S.↗

NASA NTRS2016

Degree of Ice Particle Surface Roughness Inferred from Polarimetric Observations

The degree of surface roughness of ice particles within thick, cold ice clouds is inferred from multidirectional, multi-spectral satellite polarimetric observations over oceans, assuming a column-aggregate particle habit. An improved roughness inference scheme is employed that provides a more noise-resilient roughness estimate than the conventional best-fit approach. The improvements include the introduction of a quantitative roughness parameter based on empirical orthogonal function analysis and proper treatment of polarization due to atmospheric scattering above clouds. A global 1-month data sample supports the use of a severely roughened ice habit to simulate the polarized reflectivity associated with ice clouds over ocean. The density distribution of the roughness parameter inferred from the global 1- month data sample and further analyses of a few case studies demonstrate the significant variability of ice cloud single-scattering properties. However, the present theoretical results do not agree with observations in the tropics. In the extra-tropics, the roughness parameter is inferred but 74% of the sample is out of the expected parameter range. Potential improvements are discussed to enhance the depiction of the natural variability on a global scale.

surface↗

DOE OSTI2019

Development of Waste Inventory for Probabilistic Performance Assessment Modeling - 19361

Estimation of radioactive waste inventory is a key initial step in the development of an environmental fate and transport model for performance assessment (PA) of radioactive waste sites. PA model output is often sensitive to parameters that represent inventory as a source term, yet estimation is a difficult problem. Inventory estimation for a probabilistic performance assessment (PPA) is related to, but distinct from, that performed for waste excavation, characterization, and offsite disposal. The latter appropriately focuses on obtaining the most detailed information possible on specific hazards and locations; however, these questions cannot be fully answered without intrusive site characterization efforts. This type of characterization is often not feasible when assembling information regarding a source term in a PPA model. Development of waste inventory for a PPA is one piece of a larger hierarchical model. The physical process model represented by Goldsim computer code represents one level of the hierarchy, and the second level of the hierarchy involves estimation of inputs such as inventory, transport, and risk parameters. The estimation of inputs is not a straightforward problem. The parameters of a computer model often do not have an equivalence to the physical quantities they describe, and estimation of the physical quantities themselves can be difficult due to limitations of available information. Inventory information for the West Valley Site in upstate New York is based on data from historical shipping and disposal forms. In the 1990's, the West Valley Demonstration Project (WVDP) compiled information from existing databases and historical waste records into one complete modern database. A conceptual model was then applied and used with recorded information to characterize the waste mass and radionuclide composition for each waste burial. Information was ultimately aggregated over all waste records to estimate the mass of each radionuclide within each trench, hole, or caisson. Three decades later, we revisit the master database, make updates with newly-identified information, and incorporate the conceptual model previously used for inventory estimation in the hierarchical modeling framework. We characterize uncertainty using probability distributions for waste mass and radionuclide composition at the individual shipment or individual burial level. Uncertainty incorporated at the lowest level of the hierarchy is ultimately carried through to uncertainty in PPA model endpoints. Sensitivity analysis of early model results is then applied to identify influential inputs and guide subsequent efforts towards parameters that drive risk. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Gap-filled meteorological data (2011-2020) and modeled potential evapotranspiration data from the KCOMTCRE2 WeatherUnderground weather station, from the East River Watershed, Colorado

This dataset is a gap-filled meteorological dataset (years 2011-2020) that includes modeled potential evapotranspiration from a station near the pumphouse on the East River (KCOMTCRE2 WeatherUnderground ) near the top of the ridge at the Pumphouse PLM wells. These datasets are useful as model inputs. The KOMTCRE2 station is located on Mt. Crested Butte in Colorado. More information can be found at: https://www.wunderground.com/dashboard/pws/KCOMTCRE2/table/2020-12-7/2020-12-7/daily. The purpose of this dataset is to use as input to hydrological and flow-reactive transport models as meteorological forcing. One attached file includes metadata files with definitions for all variables and units. Subsequent files include the same time series dataset, but aggregated and gap-filled at different time-stamp intervals. These include 5 minute time-series, 15 minute time-series, 30 minute time-series, 1 hour time-series, daily time-series, and hourly and daily modeled evapotranspiration.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2016

Monitoring Interannual Variation in Global Crop Yield Using Long-Term AVHRR and MODIS Observations

Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) data have been extensively applied for crop yield prediction because of their daily temporal resolution and a global coverage. This study investigated global crop yield using daily two band Enhanced Vegetation Index (EVI2) derived from AVHRR (1981-1999) and MODIS (2000-2013) observations at a spatial resolution of 0.05deg (approx.5 km). Specifically, EVI2 temporal trajectory of crop growth was simulated using a hybrid piecewise logistic model (HPLM) for individual pixels, which was used to detect crop phenological metrics. The derived crop phenology was then applied to calculate crop greenness defined as EVI2 amplitude and EVI2 integration during annual crop growing seasons, which was further aggregated for croplands in each country, respectively. The interannual variations in EVI2 amplitude and EVI2 integration were combined to correlate to the variation in cereal yield from 1982-2012 for individual countries using a stepwise regression model, respectively. The results show that the confidence level of the established regression models was higher than 90% (P value < 0.1) in most countries in the northern hemisphere although it was relatively poor in the southern hemisphere (mainly in Africa). The error in the yield predication was relatively smaller in America, Europe and East Asia than that in Africa. In the 10 countries with largest cereal production across the world, the prediction error was less than 9% during past three decades. This suggests that crop phenology-controlled greenness from coarse resolution satellite data has the capability of predicting national crop yield across the world, which could provide timely and reliable crop information for global agricultural trade and policymakers.

Crop phenology↗

DOE OSTI2013

Wavelet analysis of molecular dynamics: Efficient extraction of time-frequency information in ultrafast optical processes

New experimental techniques based on nonlinear ultrafast spectroscopies have been developed over the last few years, and have been demonstrated to provide powerful probes of quantum dynamics in different types of molecular aggregates, including both natural and artificial light harvesting complexes. Fourier transform-based spectroscopies have been particularly successful, yet “complete” spectral information normally necessitates the loss of all information on the temporal sequence of events in a signal. This information though is particularly important in transient or multi-stage processes, in which the spectral decomposition of the data evolves in time. By going through several examples of ultrafast quantum dynamics, we demonstrate that the use of wavelets provide an efficient and accurate way to simultaneously acquire both temporal and frequency information about a signal, and argue that this greatly aids the elucidation and interpretation of physical process responsible for non-stationary spectroscopic features, such as those encountered in coherent excitonic energy transport.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Ultra-High-Performance Concrete And Advanced Manufacturing Methods For Modular Construction

Small modular reactors (SMR) allow for less onsite construction, increase nuclear material security, and provide a flexible and cost-effective energy alternative. SMR can be factory-built as modular components, and shipped to desired locations for fast assembly. This project successfully developed a new class of ultra-high performance concrete (UHPC), which features a compressive strength greater than 22 ksi (150 MPa) without special treatment and self-consolidating characteristics desired for SMR modular construction. With an ultra-high strength and dense microstructure, it will facilitate rapid construction of steel plate-concrete (SC) beams and walls with thinner and lighter modules, and can withstand harsh environments and mechanical loads anticipated during the service life of nuclear power plants. In addition, the self-consolidating characteristics are crucial for the fast construction and assembly of SC modules with reduced labor costs and improved quality. Following the UHPC material development, the capacity of producing self-consolidating UHPC in mass quantities was investigated and compared to accepted self-consolidating concrete standards. With slightly adjusted mixing procedure using large-scale gravity-based mixers (compared with small-scale force-based mixer), the self-consolidating UHPC has been successfully processed at six cubic yards; the product met both minimum compressive strength requirements and self-consolidating concrete standards. Steel plate-UHPC beams (15 ft. long, 12 in. wide and 16 in. deep) and wall panels (40 in. X 40 in. X 3 in.) were then constructed using the self-consolidating UHPC without any external vibration. Quality control guidelines for producing UHPC in large scale were developed. When the concrete is replaced by UHPC in a steel plate concrete (SC) beam, it is critical to evaluate its structural behavior with both flexure and shear-governed failure modes. In recent years, SC has been widely used for buildings and nuclear containment structures to resist lateral forces induced by severe earthquakes and heavy winds. SC modules have good potential for SMR because of their cost-effectiveness and reduced construction time. However, the minimum shear reinforcement (i.e. cross tie) ratio needs to be determined for the steel plate-UHPC (S-UHPC) beams to exhibit a ductile failure mode. In this project, S-UHPC beams were designed and constructed. The beams were tested to evaluate structural capacity and identify the minimum cross ties ratios. In addition, as the bond between UHPC and steel plate is essential for ensuring structural integrity under shear and flexure, it was measured and examined in this project through digital image correlation system and smart piezoelectric aggregate sensors. Large-scale testing and finite element simulation were also performed on S-UHPC wall panels. New bond slip-based constitutive models of steel plate were developed for S-UHPC, which were used in finite element analysis program to predict S-UHPC behavior under shear. The results were well validated through experimental data. The long-term durability of UHPC were established in this project. UHPC specimens were tested under free shrinkage, restrained shrinkage, elevated temperature, water permeation, chloride diffusion, corrosion, and alkali silica reaction. UHPC has demonstrated significantly improved durability compared with control concrete specimens. This research led to a new generation of steel plate-UHPC modules for SMR that can provide large benefits to the electric power industry. Taking advantage of the high strength and durability of UHPC, their modularity and ease of assembly can address the high cost barriers of typical nuclear power plants.

42 ENGINEERING↗

DOE OSTI2017

High Performance Data Transfer for Distributed Data Intensive Sciences

We report on the development of ZX software providing high performance data transfer and encryption. The design scales in: computation power, network interfaces, and IOPS while carefully balancing the available resources. Two U.S. patent-pending algorithms help tackle data sets containing lots of small files and very large files, and provide insensitivity to network latency. It has a cluster-oriented architecture, using peer-to-peer technologies to ease deployment, operation, usage, and resource discovery. Its unique optimizations enable effective use of flash memory. Using a pair of existing data transfer nodes at SLAC and NERSC, we compared its performance to that of bbcp and GridFTP and determined that they were comparable. With a proof of concept created using two four-node clusters with multiple distributed multi-core CPUs, network interfaces and flash memory, we achieved 155Gbps memory-to-memory over a 2x100Gbps link aggregated channel and 70Gbps file-to-file with encryption over a 5000 mile 100Gbps link.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Availability Estimates of Low-Cost, Dispatch-Constrained Electricity (LDE)

This dataset accompanies the report "Potential Availability of and Supply Curves for Low-Cost, Dispatch-Constrained Electricity", which provides initial estimates of the quantity and availability of the potential low-cost, dispatch-constrained electricity (LDE) resource in the United States under scenarios with high variable renewable energy (VRE) penetrations. The analysis also estimates supply curves that can be used in subsequent analysis of the opportunity to use the LDE. The estimated LDE resource could be used for various applications that value low-cost electricity and can operate at reduced capacity factors, including electrolytic hydrogen production and carbon capture. The analysis accompanying this dataset was performed in 2018-2019 to support an analysis of "The Technical and Economic Potential of the H2@Scale Concept within the United States" ( https://www.nlr.gov/docs/fy21osti/77610.pdf ). This dataset includes the quantity of estimated LDE resource for each hour of the year in 2050, aggregated to both the national level and the ReEDS balancing area level. As described in the accompanying report, the ReEDS model was used to project the evolution of the power sector over time. Links to the mapping of ReEDS balancing areas to states and ReEDS balancing area shape files are provided. This dataset also includes the report figure data.

08 HYDROGEN↗

NASA NTRS1993

Optical effects of space weathering on lunar soils and the role of the finest fraction

Lunar soils represent a cumulative product of space weathering and as such they exhibit distinct optical alteration. In comparison to the optical properties of similar rock powders, lunar soils are darker, have weaker diagnostic absorption bands, and exhibit a characteristic red continuum slope between 0.3 and 4.5 microns. Well-developed (mature) lunar soils have a mean grain size of about 60 microns and may contain more than 50 percent agglutinates (complex, glass-welded aggregates) which were previously believed to be the principal carriers of optical alteration resulting from space weathering. A detailed analysis of the spectral reflectance properties of the bulk soil, size separates, and agglutinate separates of several Apollo and Luna soils has been undertaken to evaluate the validity of the agglutinate paradigm for optical effects of space weathering. The data and results are summarized here which indicate that the finest fraction of natural lunar soils, rather than larger complex agglutinates, carries the principal effects of optical alteration. Bidirectional reflectance spectra were obtained using the small sample configuration of the RELAB facility, which accommodates 5-30 mg of material. Spectra are displayed with the labels for individual samples listed in order of brightness.

Pieters, C. M.↗

NASA NTRS2016

Characterization of Evidence for Human System Risk Assessment

Understanding the kinds of evidence available and using the best evidence to answer a question is critical to evidenced-based decision-making, and it requires synthesis of evidence from a variety of sources. Categorization of human system risks in spaceflight, in particular, focuses on how well the integration and interpretation of all available evidence informs the risk statement that describes the relationship between spaceflight hazards and an outcome of interest. A mature understanding and categorization of these risks requires: 1) sufficient characterization of risk, 2) sufficient knowledge to determine an acceptable level of risk (i.e., a standard), 3) development of mitigations to meet the acceptable level of risk, and 4) identification of factors affecting generalizability of the evidence to different design reference missions. In the medical research community, evidence is often ranked by increasing confidence in findings gleaned from observational and experimental research (e.g., "levels of evidence"). However, an approach based solely on aspects of experimental design is problematic in assessing human system risks for spaceflight. For spaceflight, the unique challenges and opportunities include: (1) The independent variables in most evidence are the hazards of spaceflight, such as space radiation or low gravity, which cannot be entirely duplicated in terrestrial (Earth-based) analogs, (2) Evidence is drawn from multiple sources including medical and mission operations, Lifetime Surveillance of Astronaut Health (LSAH), spaceflight research (LSDA), and relevant environmental & terrestrial databases, (3) Risk metrics based primarily on LSAH data are typically derived from available prevalence or incidence data, which may limit rigorous interpretation, (4) The timeframe for obtaining adequate spaceflight sample size (n) is very long, given the small population, (5) Randomized controlled trials are unattainable in spaceflight, (6) Collection of personal and environmental data on the astronaut population may create opportunities for advanced analytics and human-environment modeling that goes beyond that achieved in isolated experimental designs; and (7) Translation of relevant research to operations is a complex, transdisciplinary enterprise in which the approach must apply across the physical, biological, behavioral, and social sciences. The approach to synthesizing evidence must address both source and fidelity of data, and reflect the most general attributes of quality of evidence in science and engineering: reliability and validity. The authors are developing a two-factor approach which includes the various kinds of evidence required to understand risks and for the integrated interpretation of all evidence that is essential to develop standards and countermeasures. A unified framework for aggregating and assessing different kinds of evidence provides a consistent, traceable, evidence-based decision-making process to translate research to operations in an environment where engineers, scientists, physicians, and managers all engage in analyzing the trade space of vehicle design, standards, requirements and solutions for spaceflight.

Steinberg, S. L.↗