Interpretation of Zn Isotope Ratio Measurements in a Complex Geochemical System
Bibliographic record
Abstract
Zinc isotope ratios were measured for pore water samples collected from a pilot-scale remediation system designed to asssess the potential benefits of promoting bacterial SO4 reduction and precipitation of metal sulfides. Samples were collected from three test cells at the Greens Creek mine (Alaska, USA), including a control cell and two treatment cells. Cells TC4 and TC7 were amended with organic carbon. The first treatment cell (TC4) contained 5 vol.% organic carbon as peat (2.5 vol. %) and spent brewing grain (2.5 vol. %), and the second treatment cell (TC7) contained 10 vol.% organic carbon as peat (5 Vol. %), spent brewing grain (2.5 vol. %) and municipal biosolids (2.5 vol. %). High concentrations of dissolved Zn (97 to 320 mg L-1 ) and SO4 near the tailings surface indicate Zn release by sphalerite [(Zn,Fe)S] oxidation. Zinc isotope ratios near the tailings surface in all three cells were similar and ranged between +0.25 and +0.35 ‰ (δ66Znavg = +0.3 ±0.05 ‰). At depths equal or below 1 m below surface, Zn concentrations were generally below 2.7 mg L-1 in TC4 and TC7 and below 7.1 mg L-1 in TC2. This decline in Zn concentrations in TC4 and TC7 is attributed bacterial SO4 reduction and concomitant alkalinity production, leading to extensive precipitation of Zn sulfide phases and potentially Zn carbonate phases. Zinc isotope measurements indicate Δ66Zn values of up to -0.35 ‰. Laboratory studies indicate precipitation of Zn sulfide phases results in preferential incorporation of 64Zn, resulting in increasingly positive δ 66Zn values, whereas precipitation of Zn carbonate leads to increasingly negative δ66Zn values. These observations suggest that precipitation of a combination of secondary sulfide and carbonate phases controls Zn mobility and isotope ratios under SO4-reducing conditions within the amended cells.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".