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Record W4286494278 · doi:10.1038/s41467-022-31894-0

Sulfur and chlorine budgets control the ore fertility of arc magmas

2022· article· en· W4286494278 on OpenAlexafffund
Carter Grondahl, Zoltán Zajacz

Bibliographic record

VenueNature Communications · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversity of Toronto
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of CanadaSociety of Economic Geologists Canada FoundationSociety of Economic Geologists Foundation
KeywordsSulfurChlorineFertilityArc (geometry)ChemistryMedicineEngineeringEnvironmental health

Abstract

fetched live from OpenAlex

Continental arc magmas supply the ore-forming element budget of most globally important porphyry-type ore deposits. However, the processes enabling certain arc segments to preferentially generate giant porphyry deposits remain highly debated. Here we evaluate the large-scale covariation of key ore-forming constituents in this setting by studying silicate melt inclusions in volcanic rocks from a fertile-to-barren segment of the Andean Southern Volcanic Zone (33-40 °S). We show that the north-to-south, fertile-to-barren gradient is characterized by a northward increase in S and Cl concentrations and a simultaneous decrease in Cu. Consequently, we suggest that the concentration of S and Cl rather than the concentration of ore metals regulates magmatic-hydrothermal ore fertility, and that the loss of volatiles prior to arrival in the upper crust impacts ore-forming potential more than magmatic sulfide saturation-related ore metal scavenging.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.225
Teacher spread0.211 · 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

Citations55
Published2022
Admission routes2
Has abstractyes

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