The relative contributions of biological and abiotic processes to carbon dynamics in subarctic sea ice
Bibliographic record
Abstract
Knowledge on the relative effects of biological activity and precipitation/dissolution of calcium carbonate (CaCO3) in influencing the air-ice CO2 exchange in sea-ice-covered season is currently lacking. Furthermore, the spatial and temporal occurrence of CaCO3 and other biogeochemical parameters in sea ice are still not well described. Here we investigated autotrophic and heterotrophic activity as well as the precipitation/dissolution of CaCO3 in subarctic sea ice in South West Greenland. Integrated over the entire ice season (71 days), the sea ice was net autotrophic with a net carbon fixation of 56 mg C m−2, derived from a sea-ice-related gross primary production of 153 mg C m−2 and a bacterial carbon demand of 97 mg C m−2. Primary production contributed only marginally to the TCO2 depletion of the sea ice (7–25 %), which was mainly controlled by physical export by brine drainage and CaCO3 precipitation. The net biological production could only explain 4 % of this sea-ice-driven CO2 uptake. Abiotic processes contributed to an air-sea CO2 uptake of 1.5 mmol m−2 sea ice day−1, and dissolution of CaCO3 increased the air-sea CO2 uptake by 36 % compared to a theoretical estimate of melting CaCO3-free sea ice. There was a considerable spatial and temporal variability of CaCO3 and the other biogeochemical parameters measured (dissolved organic and inorganic nutrients).
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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) | 0.000 | 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".