Effects of microbial processes and CaCO3 dynamics on inorganic carbon cycling in snow-covered Arctic winter sea ice
Bibliographic record
Abstract
Few combined measurements of primary and bacterial productivity exist for Arctic sea ice, particularly during winter, making it difficult to assess the relative importance of these microbial processes for carbon cycling in sea ice. Furthermore, the occurrence of calcium carbonate (CaCO3 ), though well-documented in sea ice, is poorly described for the overlying snow. To address these gaps, we investigated primary and bacterial productivity and carbon dynamics at 2 contrasting locations: (1) a landfast site, with thick snow-covered first-year sea ice, and (2) a polynya site, with thin snow-covered young (<1 wk) sea ice. Comparisons of bacterial carbon demand and primary production indicated net heterotrophy in the sea ice at both locations, with a net carbon consumption rate of 0.87 to 1.86 mg C m-2 d-1 derived from sea ice bacterial carbon demand of 0.93 to 2.00 mg C m-2 d-1 and gross primary production of 0.06 to 0.14 mg C m-2 d-1. As these microbial rates are very low, physical processes largely account for the observed CO2 depletion in the ice. High CaCO3 concentrations of 250 to 430 µmol kg-1 were measured in the snow covers which, though similar to concentrations in the underlying ice, are orders of magnitude higher than those reported from the few studies available on CaCO3 in snow. Together these results suggest that the role of biology in modulating inorganic carbon cycling in ice, which can be important in spring, is minor as compared to abiotic processes.
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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 | 0.000 | 0.000 |
| 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".