Climate patterns and phytoplankton dynamics in Antarctic latent heat polynyas
Bibliographic record
Abstract
Seasonal coherence between satellite‐derived phytoplankton parameters (chlorophyll a concentration (Chl) and phytoplankton bloom initiation time (BIT)), environmental variables (sea ice concentration, surface solar radiation, and wind speed), and climate patterns (El Niño 3.4, the Southern Annular Mode, the Pacific South America pattern, the semiannual oscillation, and the wave‐3 stationary pattern) was investigated in four latent heat polynyas (Amundsen Sea, western Ross Sea, Dumont d'Urville, and Prydz Bay) using data corresponding to 1998–2006 phytoplankton growing seasons. In general, polynyas in the western sector (i.e., Amundsen Sea and western Ross Sea) had a greater sea ice cover, lower solar radiation levels, higher and more variable Chl, and more variable and delayed (i.e., high BIT) phytoplankton blooms. Differences in Chl and BIT were mainly attributed to differences in water stratification and interannual variability of sea ice concentration caused by ice shelf calving events in the Ross Sea. Changes in solar radiation reaching the sea surface played an important role in determining phytoplankton blooms in the western Ross Sea and Prydz Bay. Stronger winds tend to benefit development of phytoplankton blooms in polynyas having more stratified waters. Sensitivity of phytoplankton to climate variability in polynyas under investigation was highly influenced by the polynya size during summer (e.g., Chl in Dumont d'Urville and BIT in the western Ross Sea). Also, the response of Chl to the same climate pattern changed with the polynya's location (e.g., correlation between Chl and El Niño 3.4 in Amundsen Sea (positive) and Dumont d'Urville (negative)).
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".