Chlorine isotope fractionation during metal-chloride complexation: Implications for metallogenic processes
Bibliographic record
Abstract
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.The complexes with higher Cl -/cation molar ratios have smaller reduced isotopic partition function ratio(i.e., β-factors), preferring to enrich the light isotope ( 35 Cl) in the complexes.For the same metal-chlorine complex configuration but differing coordination number (CNs), the metal-chlorine bond length increases with an increase in the coordination number (CNs), resulting in a decrease in the 1000lnβ value.Furthermore, a comparison of the fractionation of chlorine isotopes in CuCl(H 2 O) and CuCl(HS) reveals that sulfur donor ligand systems with longer bonds have lower 1000lnβ values, which is in good agreement with results of the study of Fujii and Albarede (2018) [3].The metal-chloride complexes in hydrothermal fluids vary depending on temperature, pressure, pH, salinity and redox state.This study helps explain the variability of chlorine isotopic compositions of fluid inclusions in ore deposits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".