MétaCan
Menu
Back to cohort
Record W4327811138 · doi:10.48550/arxiv.2303.09539

Why is EXAFS analysis for multicomponent metals so hard? Challenges and opportunities for measuring ordering in complex concentrated alloys using x-ray absorption spectroscopy

2023· preprint· en· W4327811138 on OpenAlexaff
Howie Joress, Bruce Ravel, Elaf A. Anber, Jonathan Hollenbach, Debashish Sur, Jason Hattrick‐Simpers, Mitra L. Taheri, Brian DeCost

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicX-ray Spectroscopy and Fluorescence Analysis
Canadian institutionsUniversity of Toronto
FundersOffice of Naval ResearchMultidisciplinary University Research InitiativeBrookhaven National LaboratoryOffice of ScienceU.S. Department of Energy
KeywordsExtended X-ray absorption fine structureAbsorption (acoustics)Materials scienceAlloyRange (aeronautics)Surface-extended X-ray absorption fine structureCorrosionAbsorption spectroscopyMetallurgyPhysicsOpticsComposite material

Abstract

fetched live from OpenAlex

Short range order is a critical driver of properties (e.g. corrosion resistance and tensile strength) in multicomponent alloys such as complex concentrated alloys (CCAs). Extended x-ray absorption fine structure (EXAFS) is a powerful technique well suited for quantifying this short range order.Here, we described in detail the characteristics of CCAs that make the already challenging task of analyzing EXAFS data even more difficult. We then illustrate novel paths towards robust and scalable quantitative SRO analysis which will accelerate the scientific understanding and development of CCAs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0020.005
Scholarly communication0.0030.009
Open science0.0020.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.320
GPT teacher head0.263
Teacher spread0.057 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venuearXiv (Cornell University)Same topicX-ray Spectroscopy and Fluorescence AnalysisFrench-language works237,207