Under-Ice Phytoplankton Blooms: Shedding Light on the “Invisible” Part of Arctic Primary Production
Bibliographic record
Abstract
The growth of phytoplankton at high latitudes was generally thought to begin in open waters of the marginal ice zone once the highly reflective sea ice retreats in spring, solar elevation increases, and surface waters become stratified by the addition of sea-ice melt water. In fact, virtually all recent large-scale estimates of primary production in the Arctic Ocean (AO) assume that phytoplankton production in the water column under sea ice is negligible. However, over the past two decades, an emerging literature showing significant under-ice phytoplankton production on a pan-Arctic scale has challenged our paradigms of Arctic phytoplankton ecology and phenology. This evidence, which builds on previous, but scarce reports, requires the Arctic scientific community to change its perception of traditional AO phenology and urgently revise it. In particular, it is essential to better comprehend, on small and large scales, the changing and variable icescapes, the under-ice light field and biogeochemical cycles during the transition from sea-ice covered to ice-free Arctic waters. Here, we provide a baseline of our current knowledge of under-ice blooms (UIBs), by defining their ecology and their environmental setting, but also their regional peculiarities (in terms of occurrence, magnitude, and assemblages), which is shaped by a complex AO. To this end, a multidisciplinary approach, i.e., combining expeditions and modern autonomous technologies, satellite, and modeling analyses, has been used to provide an overview of this pan-Arctic phenological feature, which will become increasingly important in future marine Arctic biogeochemical cycles.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 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".