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Record W4384210660 · doi:10.1039/d2dd00149g

Tackling data scarcity with transfer learning: a case study of thickness characterization from optical spectra of perovskite thin films

2023· article· en· W4384210660 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueDigital Discovery · 2023
Typearticle
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsUniversity of British Columbia
FundersScience and Engineering Research CouncilEnergy Market Authority of SingaporeNational Research Foundation SingaporeAgency for Science, Technology and ResearchNatural Resources CanadaMassachusetts Institute of TechnologyNational Research FoundationCanadian Natural Resources LimitedNational University of Singapore
KeywordsReflection (computer programming)Transfer of learningCharacterization (materials science)Perovskite (structure)Spectral lineData transmissionMaterials scienceScarcityThin filmTransfer (computing)Transmission (telecommunications)Computer scienceOpticsChemical engineeringNanotechnologyArtificial intelligencePhysicsEngineeringTelecommunicationsEconomics

Abstract

fetched live from OpenAlex

thicknessML predicts film thickness from reflection and transmission spectra. Transfer learning enables thickness prediction of different materials with good performance. Transfer learning also bridges the gap between simulation and experiment.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.665

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 communication0.0010.003
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.270
Teacher spread0.244 · 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