Aqueous sulfide oxidation and feldspar dissolution (geochemical reaction modeling using CHILLER).
Bibliographic record
Abstract
The oxidation of sulfides (primarily pyrite and pyrrhotite) exposed in mine waste accumulations may produce a&barbelow;cid m&barbelow;ine d&barbelow;rainage (AMD), which is characterized by a low pH, a high sulfate content, and the presence of dissolved metals. In this study, CHILLER was used to study how various feldspars (albite, anorthite and microcline) neutralize/buffer the acid generated from pyrite oxidation. Pyrite:feldspar ratios of 1:4, 1:1 and 4:1 were modeled to determine the effects of changing the pyrite:feldspar ratio. The results from the 1:1 pyrite:feldspar reaction models were explored in detail. For each reaction model, a different series of mineral pH-buffering assemblages formed from the products of the feldspar dissolution reactions, buffering the acidity to different values. Altering the pyrite:feldspar ratio (1:4, 1:1 and 4:1) had no affect on the sequence of alteration minerals formed, so the resultant buffering reactions, buffered pH values and final equilibrated pH were the same for each group of modeled reactions. (Abstract shortened by UMI.)Dept. of Earth Sciences. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1999 .L38. Source: Masters Abstracts International, Volume: 39-02, page: 0459. Adviser: Peter P. Hudec. Thesis (M.Sc.)--University of Windsor (Canada), 2000.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".