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Record W2491537477 · doi:10.1130/dnag-gna-p1.287

Arsenide Vein Silver, Uranium

2015· book-chapter· en· W2491537477 on OpenAlexaffabout
V Ruzicka, R I Thorpe

Bibliographic record

VenueGeological Society of America eBooks · 2015
Typebook-chapter
Languageen
FieldChemistry
TopicRadioactive element chemistry and processing
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsMineralUraniumUranium oreSection (typography)Volume (thermodynamics)VeinArsenideGovernment (linguistics)GeologyMineralogyMetallurgyComputer scienceMaterials scienceMedicinePhysicsSurgeryGallium arsenideOptoelectronicsPhilosophy

Abstract

fetched live from OpenAlex

This volume defines and summarizes in a comprehensive and systematic manner the essential characteristics of all economically significant types of Canadian mineral deposits. These summaries reflect the current understanding of mineral deposits and correspond closely to the definition of mineral-deposit types in common use. A large color section serves to illustrate details of some of these mineral deposits, and locations of all known deposits are presented on an oversize figure and are indexed in an appendix, as well. Like previous volumes of this type, this volume will be a long-standing premier reference for academia, industry, and government institutions alike.

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)0.000
metaresearch head score (Gemma)0.000
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: Other · Consensus signal: Other
Teacher disagreement score0.062
Threshold uncertainty score0.206

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0620.039

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 head0.036
GPT teacher head0.252
Teacher spread0.216 · 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
GenreOther

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

Citations4
Published2015
Admission routes2
Has abstractyes

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