Oceanography of <italic>Skeletonema costatum</italic> harmful algal blooms in the East China Sea using MODIS and QuickSCAT satellite data
Bibliographic record
Abstract
The East China Sea (ECS) is threatened by frequent Skeletonema costatum (S. costatum) blooms every year, which can cause severe environmental harm, as well as considerable economic losses. Remote sensing is an efficient tool for monitoring these harmful algal blooms (HABs) and studying concerned marine conditions. This study investigated two intensive S. costatum HABs in the ECS by analysis of water distribution and spatial-temporal pattern of four oceanographic parameters derived from moderate resolution imaging spectroradiometer (MODIS) and QuickSCAT satellite data using multiple remote sensing approaches (composite imagery interpretation, classification, and parameters retrieval). Results show that high chlorophyll- a (Chl-a) concentrations and net primary production (NPP) decrease from the HAB areas toward the open sea. A peak of Chl-a (>10 mgm−3) and NPP (>5000 mg · C · m−2 · d−1) are considered indicators of large-scale S. costatum blooms in the ECS. Low sea surface temperature (SST; approximately 23°C) are observed in S. costatum HAB areas. In early stages, winds in terms of direction and speed can bring nutrients to facilitate the formation of S. costatum blooms, but then sharply change into unfavorable conditions to cause the final disappearance of HABs. This study also explored multiple oceanographic explanations in the ECS from biochemical, meteorological, physical, and geological perspectives for a better understanding of such S. costatum HABs mechanisms.
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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".