Impact of sea‐ice biology on overall primary production in a biophysical model of the pan‐Arctic Ocean
Bibliographic record
Abstract
The contribution of sea‐ice biology and impact of Arctic warming on overall primary production in a Pan‐Arctic ocean model are investigated in a 57 year (1950–2006) simulation at coarse resolution using a simple ecosystem model. The ice biology model formally represents the growth and aggregation of micro algae into an ice‐water interface, nearly undisturbed by surface mixed layer dynamics. The importance of this so‐called ‘ice‐algae’ stems from their significant contribution to the total primary production (up to 50% depending on the locations, according to observations described in Gosselin et al. (1997). Simple 1D tests reveal that, depending on their initial biomass and light availability, ice algae can affect the temporal variation of surface nutrients, while they marginally impact the total primary production, or the long term position of the nutricline. The sea‐ice primary production is found in the model to be as high as 40% of the total primary production depending on the location and 7.5% for the whole Arctic. The modeled primary production of the ocean is negatively correlated to the September ice cover whereas the production in the ice is more weakly positively correlated. Because of the negative correlation between sea ice cover and pelagic primary production, the short term response to the continuing ice decline will be an increased total production as seen in the model, while the ice algae production would decline.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".