The Biogeochemical Legacy of Arctic Subglacial Sediments Exposed by Glacier Retreat
Bibliographic record
Abstract
Abstract During past periods of advance, Arctic glaciers and ice sheets overrode soil, sediments, and vegetation and buried significant stores of organic matter (OM); these glaciers are now shrinking rapidly due to climate warming. Little is known about the biogeochemical processing of the OM buried beneath glacier ice which makes the processes associated with deglaciation difficult to predict. Subglacial sediments exposed at receding glacier fronts may represent a legacy of past biogeochemical processes. Here, we analyzed sediments from retreating fronts of 19 Arctic glaciers for their mineralogical and elemental composition, contents of major nutrients, OM biomarkers (aliphatic lipids and lignin‐derived phenols), 14 C age, and microbial community structure. We show the character of the sediments is mostly determined by local glaciation history and bedrock lithology. Most subglacial sediments offer high amounts of readily bioavailable phosphorus (i.e., loose, labile, and Fe/Al P fractions) but lack readily accessible carbon substrates. The subglacial OM originated mainly from overridden terrestrial vascular plants. The results of OM biomarker analysis and 14 C dating suggest the OM substrates degrade in the subglacial environment and are reworked by the resident microbial communities. We argue the biogeochemical legacy of the perishing subglacial environments is an important determinant for the early processes of proglacial ecological succession.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".