Weathering products in glacial silt and clay: Using automated mineralogy to probe size distribution and source
Bibliographic record
Abstract
Clay minerals are ubiquitous in glacial sediment, but their source is debated. In this study, automated mineralogy is used to characterize the size distribution of weathering products from bedrock and glacial sediment collected from multiple glacier basins in the St. Elias Mountains, Yukon, Canada. In comparing sediment to bedrock, biotite and chlorite show a relative decrease in Mg-rich phases and an alteration to smectite and vermiculite, respectively, with a dependence on grain size. Plagioclase undergoes a relative decrease in calcic versus sodic components, also with a dependence on grain size. Other minor differences between rock and sediment include a change from dolomite to a more Fe-rich dolomite, an increase in the dolomite content on the edge of calcite grains, an increase in kaolinite with sodic plagioclase, an increase in laumontite with calcic plagioclase, and an increase in talc with pyroxene. Minerals likely undergo preweathering in the near-surface bedrock prior to weathering in sediment in channelized and distributed subglacial waters. Characterizing the mineral alterations that occur subglacially is a step toward identifying the chemical weathering reactions that control the balance of dissolved species in glacial meltwaters and a step toward understanding the source of clay minerals from present and past glacial environments.
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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 | 0.002 | 0.002 |
| 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".