Clay mineral precipitation and low silica in glacier meltwaters explored through reaction-path modelling
Bibliographic record
Abstract
Abstract The subglacial chemical weathering environment is largely controlled by low temperatures and the presence of freshly comminuted minerals with a high surface area. These characteristics are believed to promote dissolution processes that give rise to low silica and high Ca2+fluxes emanating from glacierized basins. We test an alternative hypothesis, that mineral precipitation reactions in the subglacial environment play an equally important role in controlling the water chemistry in glacierized basins. We analyze borehole and proglacial water chemistry from a subarctic polythermal glacier, complemented by mineral XRD analysis of suspended sediment, till and bedrock samples. In conjunction with a thermodynamic analysis of the water and mineral chemistry, we use reaction-path modelling to study the chemical enrichment of water through the glacier system. We find that the high pH of the subglacial environment is conducive to secondary mineral precipitation, and that it is not possible to balance the water chemistry using dissolution reactions alone. We show that low silica can be explained by standard weathering reactions without having to invoke mineral-leaching reactions. Our results suggest that subglacial weathering intensity may be significantly underestimated if the production of secondary minerals is not considered.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".