Vertical and horizontal variations in phytoplankton chlorophyll <scp><i>a</i></scp> in response to a looping super typhoon
Bibliographic record
Abstract
Abstract Previous studies suggested that the increase in surface chlorophyll a (Chl a) is due to nutrient upwelling or to the upward mixing of the subsurface Chl a maximum layer under the influence of tropical cyclones, while often ignoring the influence of the subsurface Chl a minimum layer and horizontal advection on Chl a. In this study, we show the important roles of the upward mixing of the subsurface Chl a minimum layer, horizontal advection, as well as the upwelling of the subsurface Chl a maximum layer, taking a looping super typhoon “Saola” in the northwest Pacific in August 2023 as an example. The temporal and spatial changes of Chl a and its physical properties were investigated by combining satellite, Argo, reanalysis, and model data. The results indicate that the combined effects of the upwelling of the subsurface Chl a maximum layer caused by wind stress curls and concurrent near‐surface wind mixing were responsible for the surface Chl a increase in the looping area during the typhoon, while the 13% increase in the depth‐integrated Chl a after the typhoon is mainly due to the nutrients brought by upwelling and subsequent biochemical processes. In the edge area affected by the typhoon, the surface Chl a decrease during the typhoon was mainly due to the upward mixing of the subsurface Chl a minimum layer (the effect of upwelling in this area is relatively weak). Furthermore, the horizontal advection led to a continuous surface Chl a decrease in the edge area after the typhoon. These findings could enhance understanding of Chl a dynamics post‐tropical cyclones, aiding marine ecosystem prediction.
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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".