Chemical depletion of sediment under the Greenland Ice Sheet
Bibliographic record
Abstract
Abstract The chemical composition of sediment sampled from a network of ice boreholes contacting the bed of the ablation zone of the Greenland Ice Sheet is compared to the composition of intact bedrock clasts. This sediment is enriched in silica and depleted in reactive cations compared to the underlying bedrock. In order to test whether these differences result from grain size biases either in sample collection or due to natural sorting, sediment samples were analyzed by grain size aliquots. Enrichment of silicon and depletion of cations is consistent across grain size classes and the compositions of bedrock and subglacial sediment are statistically separate. The difference in composition between subglacial sediment and rock aligns closely with the composition of dissolved solutes in waters sampled from the same field sites and is dissimilar to the composition of the sites' suspended sediment. This implies that chemical weathering rather than disproportionate physical removal of friable minerals is responsible for the compositional differences between rock and sediment. Mass balance analysis implies 3–10% of the sediment's mass is lost to solute dissolution, with approximately double that amount precipitated as clay minerals (a large portion of which may have been physically expelled). This result implies that temperate ice sheet subglacial environments may be more chemically active than previously realized. Copyright © 2016 John Wiley & Sons, Ltd.
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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) | 0.000 | 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".