Size Structure of Primary Producers in the Marginal Ice Zone of the European Arctic in Summer
Bibliographic record
Abstract
Abstract Primary production (PP) and the chlorophyll-a concentration (chl-a) in the European Arctic in the summer of 2020–2021, where continued climatic warming and increased “Atlantification” accelerate the sea ice losses, are discussed. The maximum integrated PP and the total chl-a content were observed in the marginal ice zone (MIZ) of the Barents Sea under weakened stratification of the water column and reached 1109 mgC m–2 day–1 and 118 mg m–2. Near the ice edge in the Nansen Basin, the main part of PP formed in the upper mixed layer and did not exceed 469 mgC m–2 day–1; the chl-a content reached 56 mg m–2. The early and late stages of phytoplankton bloom in the MIZ were characterized by the leading role of picophytoplankton in carbon fixation. Large centric diatoms, microphytoplankton, were recorded to dominate in the MIZ at the stage of peak bloom in 2020 under the dense ice cover of the Nansen Basin. A similar phenomenon was observed earlier only in the Arctic shelf seas and was not recorded in the high-latitude basins of the Arctic Ocean. With the sparse ice cover of the Nansen Basin in 2021, the main primary producers were pico- and nanophytoplankton. The low variability of assimilation numbers (1.7 ± 0.3 mgC mg chl-a–1 h–1) at all bloom stages indicates indirectly the acclimatization of different species of phytoplankton to the environmental changes. The ecological flexibility of the primary production link of the MIZ ecosystems in the studied seas of the European Arctic during the period of climate changes is confirmed.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".