Biogeochemical weathering under ice: Size matters
Bibliographic record
Abstract
The basal regions of continental ice sheets are gaps in our current understanding of the Earth's biosphere and biogeochemical cycles. We draw on existing and new chemical data sets for subglacial meltwaters to provide the first comprehensive assessment of sub‐ice sheet biogeochemical weathering. We show that size of the ice mass is a critical control on the balance of chemical weathering processes and that microbial activity is ubiquitous in driving dissolution. Carbonate dissolution fueled by sulfide oxidation and microbial CO2 dominate beneath small valley glaciers. Prolonged meltwater residence times and greater isolation characteristic of ice sheets lead to the development of anoxia and enhanced silicate dissolution due to calcite saturation. We show that sub‐ice sheet environments are highly geochemically reactive and should be considered in regional and global solute budgets. For example, calculated solute fluxes from Antarctica (72–130 t yr−1) are the same order of magnitude as those from some of the world's largest rivers and rates of chemical weathering (10–17 t km−2 yr−1) are high for the annual specific discharge (2.3–4.1 × 10−3 m). Our model of chemical weathering dynamics provides important information on subglacial biodiversity and global biogeochemical cycles and may be used to design strategies for the first sampling of Antarctic Subglacial Lakes and other sub‐ice sheet environments for the next decade.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".