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Introducing “Identification Probability” for Automated and Transferable Assessment of Metabolite Identification Confidence in Metabolomics and Related Studies
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Multidimensional mass profiles increase confidence in bacterial identification when using low-resolution mass spectrometers
A novel multidimensional scan technique combining MS 1 and MS/MS scans greatly enhances bacterial differentiation from low resolution data for clinical and field-forward applications.
Building confidence in models for complex barrier systems for radionuclides
The modeling and simulation of the Cement-clay Interaction-Diffusion field (CI-D) experiment at the Mont Terri site in Switzerland presented here demonstrates that it is possible to capture the multiscale physical and chemical features of natural and engineered barrier systems for radionuclides. The simulations are successfully carried out with the newly developed CrunchODiTi high-performance computing software that accounts for multiple continua, including a continuum representing the electrical double layer (EDL) developed along negatively charged clay particles in clay rock. The simulation also accounts for both the complex three-dimensional (3D) geometry, expected as the norm in a geological waste repository, and the anisotropy of the geological formation. In addition, the high resolution of the model makes it possible to include "skin effects" developed at the interface between highly reactive materials, in this case between the high pH cement and the circumneutral but electrostatic Opalinus Clay. The successful history matching with the field experiment demonstrates that the distinct geochemical and physical properties of the cement and the Opalinus Clay in the CI-D experiment can be accounted for. Such analyses are essential for developing a defensible safety case for the underground storage of radioactive waste.
Confidently Comparing Estimates with the c-value
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Confident detection of doubly ionized thorium in the extreme Ap star CPD-62° 2717
Despite the Universe containing primordial thorium (Th) of sufficient abundance to appear in stellar spectra, detection of Th has to date been tentative and based on just a few weak and blended lines. Here, we present convincing evidence not only for the first Th detection in a magnetic chemically peculiar Ap star but also for the first detection of Th iii in a stellar spectrum. CPD-62° 2717 was initially recognized as a highly magnetized Ap star due to resolved magnetically split lines captured in H-band spectra from the SDSS/APOGEE survey. The star was subsequently pinpointed as extraordinarily peculiar when careful inspection of the H-band line content revealed the presence of five lines of Th iii, none of which are detected in the other ∼1500 APOGEE-observed Ap stars. Follow-up with the VLT + UVES confirmed a similarly peculiar optical spectrum featuring dozens of Th iii lines, among other peculiarities. Unlike past claims of Th detection, and owing to high-resolution observations of the strong (∼8–12 kG) magnetic field of CPD-62° 2717, the detection of Th iii can in this case be supported by matches between the observed and theoretical magnetic splitting patterns. Comparison of CPD-62° 2717 to stars for which Th overabundances have been previously reported (e.g. Przybylski’s Star) indicates that only for CPD-62° 2717 is the Th detection certain. Along with the focus on Th iii, we use time series measurements of the magnetic field modulus to constrain the rotation period of CPD-62° 2717 to ∼4.8 yr, thus establishing it as a new example of a superslowly rotating Ap star.
Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitments
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Demography, dynamics and data: building confidence for simulating changes in the world's forests
Vegetation demographic models (VDMs) are advanced tools for simulating forest responses to climate and land-use changes, and are essential for projecting carbon cycling and large-scale forest management strategies. Despite their increasing incorporation into Earth System Models, VDMs differ in their demographic assumptions, with no prior quantitative comparison of their performance. We benchmarked nine VDMs against observational data from boreal, temperate and tropical sites, assessing their accuracy in predicting tree growth, carbon turnover, biomass stocks and size distributions. Models were simulated under consistent climate conditions with postdisturbance recovery monitored for at least 420 yr. Postdisturbance carbon recovery trajectories showed significant variability while remaining within observational ranges. Initial regrowth rates varied substantially (0.03-0.60, 0.18-0.70 and 0.35-1.10 kgCm-2 yr-1 for boreal, temperate and tropical sites, respectively), influenced by each model's initial forest state. Models captured mature forest carbon content but showed compensating effects between overestimated growth and underestimated mortality rates. This first multi-model benchmarking identifies growth and mortality rates as critical calibration targets and highlights the need to refine postdisturbance establishment conditions for model development. We outline specific benchmarking variables needed to improve predictions of forest responses to environmental change.
Confidence-Interval Construction with Non-I.I.D. Spot-Checking Trials & its Application in Quantum Information
We develop a method to estimate the sum of conditional means of a sequence of random variables given our access to only a subsequence by spot-checking. The method works with non-independent-and-identically-distributed (non-i.i.d.) random variables and can be applied for certifying ongoing quantum information tasks.
Trust your gut: Establishing confidence in gastrointestinal models - An overview of the state of the science and contexts of use
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The Profiled Feldman-Cousins method for confidence interval estimation in the presence of nuisance parameters [Poster]
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Enhancing Confidence in a Software Ecosystem through Complementary Layers of Software Testing
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The Impacts of Explainability and Confidence on User Trust of a Digital Assistant
This is one of several manuscripts documenting the outcomes of the Trust in VUI project. We will request sponsor approval, and then plan to submit to the ESARDA Bulletin.
Phenotyping Alfalfa (Medicago sativa L.) Root Structure Architecture via Integrating Confident Machine Learning with ResNet-18
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Data and code for: Combining eddy covariance towers, field measurements, and the MEMS 2 ecosystem model improves confidence in the climate impacts of bioenergy with carbon capture and storage
BECCS demands extensive land use change that may create positive or negative radiative forcing impacts upstream of the BECCS facility through changes to in situ greenhouse gas fluxes and land surface albedo. When quantifying these upstream climate impacts, even at a single site, different methods can give different estimates. Here we show how three common methods for estimating the net ecosystem carbon balance of bioenergy crops established on former grassland or former cropland can differ in their central estimates and uncertainty.
Improving the Confidence in Retrievals of Vertical Distributions of Cloud Condensation Nuclei Number Concentration from ARM Supported by Aircraft In Situ Observations
Accurate quantification of the vertical distribution of cloud condensation nuclei (CCN) number concentrations is critical for improving our understanding of aerosol–cloud interactions. Ground-based Raman lidars operated by the Atmospheric Radiation Measurement (ARM) program, together with surface CCN measurements, are used to retrieve vertically resolved CCN number concentrations (Retrieved Number concentration of CCN, RNCCN). These retrievals rely on several assumptions, including that aerosol composition is vertically homogeneous. To assess this assumption, we developed and tested a framework to infer the dominant aerosol classes/types at different altitudes. This was done by applying a k-Nearest-Neighbors (kNN) algorithm to lidar ratio and linear depolarization ratio measurements from Raman lidar. We evaluated the framework using aircraft aerosol and CCN measurements from the ARM Holistic Interactions of Shallow Clouds, Aerosols, and Land Ecosystems (HI-SCALE) field campaign. The results show that RNCCN performance degrades as vertical aerosol complexity increases, i.e., RNCCN agrees with the aircraft CCN in vertically homogeneous conditions, but closure decreases in layered aerosol structures. To generalize beyond individual examples, we introduce a metric (heterogeneity index) that quantifies the vertical complexity by assessing the variation of inferred aerosol classes/types. Case-level statistics show a tendency for RNCCN and aircraft differences to increase with this metric. By detecting retrievals that are likely compromised by aerosol vertical heterogeneity, the proposed framework improves the interpretability and effective use of RNCCN used for long-term evaluation of models and aerosol–cloud interactions.
COMPARING FRAGMENT SIZE DISTRIBUTIONS: CALCULATION OF THE CONFIDENCE WEIGHTED HAZARD DIFFERENCE BETWEEN DISTRIBUTIONS
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