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Record W4393538173 · doi:10.5281/zenodo.7324761

Lithium Isotope Fractionation during Intensive Felsic Magmatic Differentiation

2022· dataset· en· W4393538173 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

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typedataset
Languageen
FieldEngineering
TopicMining and Gasification Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsFelsicFractionationIsotopeGeochemistryLithium (medication)Igneous differentiationChemistryIsotope fractionationIsotopes of lithiumGeologyBiologyIgneous rockNuclear physicsChromatographyIonPhysicsMaficIon exchangeOrganic chemistry

Abstract

fetched live from OpenAlex

All the isotope and element data of the manuscript "Lithium isotope fractionation during intensive felsic magmatic differentiation". Table 1 Major elemental compositions (in wt.%) for granites from the Xihuashan and Yaogangxian plutons <strong>Table 2</strong> Li isotopic and selected trace elemental compositions for granites from the Xihuashan and Yaogangxian plutons <strong>Table 3</strong> Li concentration and Li isotopic composition of minerals separated from granites and greisen of Xihuashan pluton. Table S1. The analyzed International reference materials Table S2. Chemical compositions of mica from granites in the Xihuashan pluton Table S3. Major and trace element compositions of K-feldspar from granites in the Xihuashan pluton. Table S4. Trace element compositions of zircon from granites in the Xihuashan pluton Table S5. Parameters used for the Rayleigh crystal fractionation modeling

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.070
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0740.003

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 head0.023
GPT teacher head0.221
Teacher spread0.198 · 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