Large‐scale modeling of primary production and ice algal biomass within arctic sea ice in 1992
Bibliographic record
Abstract
An ice ecosystem model was coupled to a global dynamic sea ice model to assess large‐scale variability of primary production and ice algal biomass within arctic sea ice. The component models are the Physical Ecosystem Model (PhEcoM) ice ecosystem model and the Los Alamos Sea Ice Model (CICE). Simulated annual arctic sea ice primary production was 15.1 Tg C; within the range of 9 to 73 Tg C estimated using in situ data. The amount of C fixed was >3 Tg C month−1 for March, April, and May. The Bering Sea, Arctic Ocean basins, and the Canadian Archipelago/Baffin Bay were the most productive regions on an annual basis, contributing approximately 24, 18, and another 18%, respectively. High production in the Bering Sea was due to high daily production rates, while the large sea ice coverage in the Canadian Archipelago/Baffin Bay and, in particular, the Arctic Ocean basins resulted in their considerable contribution to sea ice primary production. The simulated trends, patterns, and seasonality of ice algae agree reasonably well with very limited observations. In the model, ice growth rate controls the availability of nutrients to sea ice algae, such that ocean nutrient supply is of secondary importance to ice algal growth. The numerical model results suggest that ice melt rate, which determines the proportional rate of ice algal release, controls the termination of the bloom on large scales. The model described advances the role of sea ice algae in biogeochemical cycling within global climate models.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".