MétaCan
Menu
← Back to cohort
Record W6930047688 · doi:10.5281/zenodo.12759092

Datasets of the manuscript "Mixed-valence state in the dilute-impurity regime of La-substituted SmB6"

2024· dataset· en· W6930047688 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2024
Typedataset
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAngle-resolved photoemission spectroscopyState (computer science)X-ray absorption spectroscopyScalingBinary numberPhoton

Abstract

fetched live from OpenAlex

You can find here the ARPES and XAS data used in the paper "Mixed-valence state in the dilute-impurity regime of La-substituted SmB6" by M. Zonno et al. . Data are in Igor Pro binary format. ARPESn5d_21eV and ARPESn5d_67eV are the ARPES n5d data for the two photon energies used. The corresponding Sm concentration scalings are found in ARPES_SmDoping_21eV and ARPES_SmDoping_67eV, respectively. XAS_07, XAS_13, XAS_20, XAS_30, XAS_70, XAS_90, XAS_97, XAS_100 are the XAS waves for the different Sm concentrations measured. The energy scaling is found in XAS_Energy.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.062
Threshold uncertainty score0.206

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0620.082

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 head0.030
GPT teacher head0.247
Teacher spread0.217 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)→Same topicMarine animal studies overview→French-language works237,207→