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Record W4312360341 · doi:10.46427/gold2022.10685

Chlorine isotope fractionation during metal-chloride complexation: Implications for metallogenic processes

2022· article· en· W4312360341 on OpenAlexaff
Hai–Zhen Wei, Anthony E. Williams‐Jones, Shao‐Yong Jiang, Xi Liu, Jianjun Lu

Bibliographic record

VenueGoldschmidt2022 abstracts · 2022
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsMcGill University
Fundersnot available
KeywordsFractionationChlorineIsotopeIsotopes of chlorineChlorideMetalChemistryIsotope fractionationGeologyRadiochemistryGeochemistryInorganic chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Ore-forming hydrothermal fluids transport metals in the Earth's crust by forming complex ionic and molecular species involving ligands such as Cl -, HS -, and OH - (Seward et al., 2014) [1]. The most important of these ligands is Cl -, which in HSAB theory is a borderline base that can be complexed with both hard and soft cations (metal ions). Thus, its behavior during complexation is of great importance for understanding metal mobilization by hydrothermal fluids (e.g., Williams-Jones and Migdisov, 2014[2]). In order to quantify the extent of chlorine isotope fractionation in hydrothermal fluids, we have investigated the behavior of aqueous Zn 2+ , Pd 2+ , Cu + , Ag + , Au + , Ni 2+ , Pb 2+ , Fe 2+ -chloride complexes at elevated temperature and pressure.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.691
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.029
GPT teacher head0.251
Teacher spread0.222 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations0
Published2022
Admission routes1
Has abstractyes

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