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Record W2785709093 · doi:10.1155/2018/5854829

Origins of Chalcocite Defined by Copper Isotope Values

2018· article· lv· W2785709093 on OpenAlexaff
Ryan Mathur, Hendrik Falck, Е. В. Белогуб, Jack E. Milton, Melissa L. Wilson, Andrew L. Rose, Wayne Powell

Bibliographic record

VenueGeofluids · 2018
Typearticle
Languagelv
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsGovernment of Northwest Territories
FundersPennsylvania State UniversityUniversity of Arizona
KeywordsGeologyMaterials science

Abstract

fetched live from OpenAlex

The origin of chalcocite is explored through a comparison of the copper isotope values of this mineral from supergene enrichment, sedimentary copper/red bed, and high-temperature hypogene mineralization around the world. Data from the literature and the data presented here (n=361) reveal that chalcocite from high-temperature mineralization has the tightest cluster of values of δCu65=0±0.6 in comparison to sedimentary copper/red bed δCu65=-0.9±1.0 and supergene enrichment δCu65=+1.9±1.8 . Although the errors of the means overlap, large portions of the data lie in different values, allowing for distinguishing ranges for δCu65 of <−1 ‰ for sedimentary copper/red bed, between -1 and +1 for high-temperature hypogene, and >+1 for supergene enrichment chalcocite. The copper isotope values of sedimentary copper/red bed and supergene enrichment chalcocite are caused by redox reactions associated with the dissolution and transport of copper, whereas the tighter range of copper isotope values for hypogene minerals is associated with processes active with equilibrium conditions.

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)0.001
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 score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0020.000
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 head0.015
GPT teacher head0.250
Teacher spread0.235 · 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

Citations25
Published2018
Admission routes1
Has abstractyes

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