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Record W4366481753 · doi:10.1016/j.physb.2023.414905

Exploring the candidacy of Mo<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si8.svg" display="inline" id="d1e508"><mml:msub><mml:mrow/><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>x</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:msub></mml:math> A<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si9.svg" display="inline" id="d1e525"><mml:msub><mml:mrow/><mml:mrow><mml:mi>x</mml:mi></mml:mrow></mml:msub></mml:math> X2 (A = [Cr, Ta], X = S) for photodetection solicitations: Showcasing the DFT predictions of the structural, elastic, and optoelectronic properties

2023· article· lv· W4366481753 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePhysica B Condensed Matter · 2023
Typearticle
Languagelv
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsUniversity of Alberta
FundersPrincess Nourah Bint Abdulrahman University
KeywordsDopingChalcogenMaterials scienceStability (learning theory)ImpurityDensity functional theoryMolybdenumMonolayerComputer scienceComputational chemistryNanotechnologyCrystallographyPhysicsChemistryOptoelectronicsMachine learningQuantum mechanics

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.739
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0010.003
Science and technology studies0.0050.005
Scholarly communication0.0040.004
Open science0.0050.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.1460.003

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 head0.026
GPT teacher head0.247
Teacher spread0.221 · 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