Influence of Typhoon Matsa on Phytoplankton Chlorophyll-a off East China
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
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 it