Modeling ice algal growth and decline in a seasonally ice‐covered region of the Arctic (Resolute Passage, Canadian Archipelago)
Bibliographic record
Abstract
We have developed a coupled snow‐ice–ice algae model to investigate the importance of different ice algal growth limitation terms, as well as different loss terms, in regulating the ice algal biomass accumulation at the bottom of landfast ice in the Canadian Archipelago. The model results are compared with data collected from May to July 2002 at a station near Resolute in Barrow Strait. Our results show that ice algae are light limited at the beginning of the bloom, then fluctuate between light and nutrient limitation, finally remaining nutrient limited toward the end of the bloom. The fortnightly tide modulates the ice algal biomass through the supply of nutrient to the ice algal layer but mainly through modulation of the bottom ice melt rate. We also demonstrate that the bottom ice melt rate regulates the maximum biomass attained in the region and that a rapid increase in ice temperature can lead to a significant decline in ice algal biomass. The eventual termination of the bloom is triggered by melting of the snow cover and results from (1) increased ice algal losses due to high bottom ice melt rate and (2) decreased ice algal growth due to nutrient limitation caused by the formation of a meltwater lens below the ice. Finally, our results show that the snow cover controls the length of the bloom, such that earlier snowmelt that is expected to accompany climate warming may lead to a reduction in ice algal production.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".