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Record W2886660332 · doi:10.5382/econgeo.2018.4587

THE ORIGIN OF THE ZHANGJIALONG TUNGSTEN DEPOSIT, SOUTH CHINA: IMPLICATIONS FOR W-Sn MINERALIZATION IN LARGE GRANITE BATHOLITHS

2018· article· en· W2886660332 on OpenAlexaffabout
Shunda Yuan, Anthony E. Williams‐Jones, Jingwen Mao, Panlao Zhao, Dongliang Zhang

Bibliographic record

VenueEconomic Geology · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsMcGill University
FundersNational Key Research and Development Program of ChinaChina Institute of Atomic EnergyChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsMetallogenyBeijingChinaBatholithGeologyMineral explorationMineral resource classificationChinese academy of sciencesMineralization (soil science)GeochemistryGeological surveyLibrary scienceMining engineeringEarth scienceArchaeologyHistoryGeophysicsPaleontologyComputer scienceTectonics

Abstract

fetched live from OpenAlex

The Nanling region is the largest W-Sn metallogenic district on Earth and hosts several giant W-Sn deposits, all except one of which are spatially and genetically associated with highly evolved Mesozoic granitic stocks. Volumetrically, however, Caledonian granites (Paleozoic), mainly batholiths, approach their Mesozoic equivalents in importance and have been the target of recent exploration. [...] This suggests that the metallogenic potential of the large granitic batholiths is limited, and that W-Sn deposits hosted within granite batholiths are likely to be genetically related to highly evolved granitic stocks that in some cases have not been exposed.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.212
Teacher spread0.201 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations182
Published2018
Admission routes2
Has abstractyes

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