Environmental conditions favoring coccolithophore blooms in subarctic and arctic seas: a 20-year satellite and multi-dimensional statistical study
Bibliographic record
Abstract
Producing very extensive blooms in the world’s oceans in both hemispheres, a coccolithophore E. huxleyi affects both marine ecology and carbon fluxes at the atmosphere-ocean interface. In turn, it is subject to impacts of multiple co-acting environmental forcings responsible for spatio-temporal dynamics in E. huxleyi blooms. To reveal the individual importance of each forcing factor (FF) that is known to significantly control the extent and intensity of E. huxleyi blooms, the 1998-2016 spaceborne time series of sea surface temperature and salinity, incident photosynthetically active radiation, and the Ekman depth relevant to the North, Norwegian, Greenland, Labrador, Barents and Bering seas were employed. The descriptive statistical approach showed that E. huxleyi phytoplankton blooms were capable of arising and developing within wide but expressly sea-specific FFs ranges. Sea-specific FFs ranges, within which the blooms are particularly extensive were identified. The Random Forest Classifier (RFC) allowed to reliably rank the FFs in terms of their role in E. huxleyi bloom spatiotemporal dynamics in each target sea. High prediction ability of RFC modelling (>70%) confirms the adequacy of the developed FFs prioritization models. Although the parameters of the carbon chemistry system per se were beyond consideration, however, over the twenty years of observations, the prioritized FFs have not failed to explain the registered patterns of the spatial extent of and particulate inorganic carbon content in E. huxleyi blooms. Also, several verifications (pastcasts) showed a high degree of their consistency with the observations. Collectively, these results tell in favor of sufficiency of the FFs employed.
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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) | 0.000 | 0.001 |
| 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) | 0.000 | 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".