Role for <scp>A</scp>tlantic inflows and sea ice loss on shifting phytoplankton blooms in the <scp>B</scp>arents <scp>S</scp>ea
Bibliographic record
Abstract
Abstract Phytoplankton blooms in the Barents Sea are highly sensitive to seasonal and interannual changes in sea ice extent, water mass distribution, and oceanic fronts. With the ongoing increase of Atlantic Water inflows, we expect an impact on these blooms. Here, we use a state‐of‐the‐art collection of in situ hydrogeochemical data for the period 1998–2014, which includes ocean color satellite‐derived proxies for the biomass of calcifying and noncalcifying phytoplankton. Over the last 17 years, sea ice extent anomalies were evidenced having direct consequences for the spatial extent of spring blooms in the Barents Sea. In years of minimal sea ice extent, two spatially distinct blooms were clearly observed: one along the ice edge and another in ice‐free water. These blooms are thought to be triggered by different stratification mechanisms: heating of the surface layers in ice‐free waters and melting of the sea ice along the ice edge. In years of maximal sea ice extent, no such spatial delimitation was observed. The spring bloom generally ended in June when nutrients in the surface layer were depleted. This was followed by a stratified and oligotrophic summer period. A coccolithophore bloom generally developed in August, but was confined only to Atlantic Waters. In these same waters, a late summer bloom of noncalcifying algae was observed in September, triggered by enhanced mixing, which replenishes surface waters with nutrients. Altogether, the 17 year time‐series revealed a northward and eastward shift of the spring and summer phytoplankton blooms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.002 | 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".