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Record W4309625828 · doi:10.1016/j.patter.2022.100634

Multivariate hyperspectral data analytics across length scales to probe compositional, phase, and strain heterogeneities in electrode materials

2022· article· en· W4309625828 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePatterns · 2022
Typearticle
Languageen
FieldMaterials Science
TopicTransition Metal Oxide Nanomaterials
Canadian institutionsnot available
FundersWestern Economic Diversification CanadaNatural Sciences and Engineering Research Council of CanadaNational Science FoundationCanadian Institutes of Health ResearchBundesministerium für Bildung und ForschungNational Institutes of HealthNational Institute of Food and AgricultureUniversity of SaskatchewanWelch FoundationU.S. Department of TransportationNeuroförbundetU.S. Department of Homeland SecurityNational Research Council CanadaNational Sleep Foundation
KeywordsHyperspectral imagingPrincipal component analysisMultivariate statisticsCluster analysisPhase (matter)Singular value decompositionElectrodeComputer scienceMaterials scienceBiological systemArtificial intelligencePhysicsMachine learning

Abstract

fetched live from OpenAlex

. Phase maps from single-particle measurements are used to reconstruct directional stress profiles showcasing the distinctive insights accessible from a standards-informed application of high-dimensional chemical imaging.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.066
GPT teacher head0.346
Teacher spread0.279 · 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