The evolution of light and vertical mixing across a phytoplankton ice-edge bloom
Bibliographic record
Abstract
During summer, phytoplankton can bloom in the Arctic Ocean, both in open water and under ice, often strongly linked to the retreating ice edge. There, the surface ocean responds to steep lateral gradients in ice melt, mixing, and light input, shaping the Arctic ecosystem in unique ways not found in other regions of the world ocean. In 2016, we sampled a high-resolution grid of 135 hydrographic stations in Baffin Bay as part of the Green Edge project to study the ice-edge bloom, including turbulent vertical mixing, the under-ice light field, concentrations of inorganic nutrients, and phytoplankton biomass. We found pronounced differences between an Atlantic sector dominated by the warm West Greenland Current and an Arctic sector with surface waters originating from the Canadian archipelago. Winter overturning and thus nutrient replenishment was hampered by strong haline stratification in the Arctic domain, whereas close to the West Greenland shelf, weak stratification permitted winter mixing with high-nitrate Atlantic-derived waters. Using a space-for-time approach, we linked upper ocean dynamics to the phytoplankton bloom trailing the retreating ice edge. In a band of 60 km (or 15 days) around the ice edge, the upper ocean was especially affected by a freshened surface layer. Light climate, as evidenced by deep 0.415 mol m–2 d–1 isolumes, and vertical mixing, as quantified by shallow mixing layer depths, should have permitted significant net phytoplankton growth more than 100 km into the pack ice at ice concentrations close to 100%. Yet, under-ice biomass was relatively low at 20 mg chlorophyll-a m–2 and depth-integrated total chlorophyll-a (0–80 m) peaked at an average value of 75 mg chlorophyll-a m–2 only around 10 days after ice retreat. This phenological peak may hence have been the delayed result of much earlier bloom initiation and demonstrates the importance of temporal dynamics for constraints of Arctic marine primary 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".