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Record W2998294334 · doi:10.2138/gselements.15.6.377

Kimberlites: From Deep Earth to Diamond Mines

2019· article· en· W2998294334 on OpenAlexaff
Andrea Giuliani, D. Graham Pearson

Bibliographic record

VenueElements · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsKimberliteGeologyDiamondGeochemistryMantle (geology)LithosphereEarth scienceIgneous rockVolcanologyEarth (classical element)PaleontologyVolcanoTectonicsChemistry

Abstract

fetched live from OpenAlex

Kimberlites are rare, enigmatic, low-volume igneous rocks. They are highly enriched in magnesium, volatiles (CO2 and H2O) and incompatible trace elements and are thought to be the most deeply derived (>150 km) magmatic rocks on Earth. Kimberlites occur in ancient and thick continental lithosphere, forming intrusive sheets and composite pipes, commonly in clusters. Despite their rarity, kimberlites have attracted considerable attention because they entrain not only abundant mantle fragments but also diamonds, which can provide a uniquely rich picture of the deep Earth. This issue summarises current thinking on kimberlite petrology, geochemistry, and volcanology and outlines the outstanding questions on the genesis of kimberlites and associated diamond mines.

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: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.007
GPT teacher head0.189
Teacher spread0.182 · 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
GenreReview

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

Citations84
Published2019
Admission routes1
Has abstractyes

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