Influence of Typhoon Matsa on Phytoplankton Chlorophyll-a off East China
Bibliographic record
Abstract
Typhoons can cause strong disturbance, mixing, and upwelling in the upper layer of the oceans. Rich nutrients from the subsurface layer can be brought to the euphotic layer, which will induce the phytoplankton to breed and grow rapidly. In this paper, we investigate the impact of an intense and fast moving tropical storm, Typhoon Matsa, on phytoplankton chlorophyll-a (Chl-a) concentration off East China. By using satellite remote sensing data, we analyze the changes of Chl-a concentration, Sea Surface Temperature (SST) and wind speed in the pre- and post-typhoon periods. We also give a preliminary discussion on the different responses of the Chl-a concentration between nearshore and offshore waters. In nearshore/coastal regions where nutrients are generally rich, the Chl-a maximum occurs usually at the surface or at the layer close to the surface. And, in offshore tropical oligotrophic oceans, the subsurface maxima of Chl-a exist usually in the stratified water column. In an offshore area east of Taiwan, the Chl-a concentration rose gradually in about two weeks after the typhoon. However, in a coastal area north of Taiwan high Chl-a concentration decreased sharply before landfall, rebounded quickly to some degree after landfall, and restored gradually to the pre-typhoon level in about two weeks. The Chl-a concentration presented a negative correlation with the wind speed in the nearshore area during the typhoon, which is opposite to the response in the offshore waters. The phenomena may be attributable to onshore advection of low Chl-a water, coastal downwelling and intensified mixing, which together bring pre-typhoon surface Chl-a downward in the coastal area. In the offshore area, the typhoon may trigger increase of Chl-a concentration through uptake of nutrients by typhoon-induced upwelling and entrainment mixing.
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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 | 0.000 |
| 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".