A pan-Arctic perspective on the influence of ice algae on sea-ice nutrient concentrations
Bibliographic record
Abstract
Sea-ice algae account for a substantial part of annual primary production in ice-covered waters and are an important component of the Arctic marine food web. With climate-induced changes to snow and sea-ice cover and their impact on the surface ocean, such as earlier melt, thinner ice, and increased upper-ocean stratification, a shift toward earlier and more extensive nutrient limitation on ice algal growth can be expected. Therefore, increasing our understanding of the processes governing nutrient supply and uptake by sea-ice algae is essential. Here, we compiled a pan-Arctic dataset of concentrations of sea-ice and sub-ice nutrients and sea-ice chlorophyll a (chl a) to assess their regional and seasonal variability, as well as the relationship of sea-ice algae and nutrient dynamics in the Arctic Ocean. This dataset indicates that bottom sea-ice nutrient and chl a concentrations were highest in the central Canadian Arctic Archipelago (Resolute Passage) due to tidal-driven mixing at the ocean-ice interface, and lowest in the Arctic Ocean basins. At the regional scale, Pacific and Atlantic Water influence variability in sea-ice and sub-ice nutrient concentrations. Significant positive relationships of bottom sea-ice nutrient versus chl a concentrations were ubiquitous across the Arctic during the ice algal bloom, suggesting intracellular nutrient storage as an important mechanism to support ice algal growth. This relationship in turn alters nutrient ratios within the sea ice relative to sub-ice waters, decreasing NOx:PO4 ratios, while increasing NOx:Si(OH)4 ratios. In contrast, bottom sea-ice nutrient-chl a relationships were less common and sometimes negative when nutrient concentrations were low, likely reflecting nutrient limitation. In conclusion, we have demonstrated a pan-Arctic, yet regionally specific, influence of the ice algal community on bottom sea-ice nutrient concentrations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".