Effect of a fast‐moving tropical storm <scp>W</scp>ashi on phytoplankton in the northwestern <scp>S</scp>outh <scp>C</scp>hina <scp>S</scp>ea
Bibliographic record
Abstract
Abstract Tropical cyclones may augment nutrients in the ocean surface layer through mixing, entrainment, and upwelling, triggering phytoplankton blooms in oligotrophic waters such as the South China Sea (SCS). Previous studies focused mainly on responses of marine environments to strong or slow‐moving typhoons in the SCS. In this study, we analyze variations of chlorophyll a (Chl a) and oceanic conditions in the continental shelf region east of Hainan Island during the fast‐moving tropical storm Washi and investigate its influences on phytoplankton bloom and related dynamic mechanisms. Results indicate that there was significant variation of Chl a concentration in the continental shelf region, with low values (about 0.1 mg m−3) before the storm and a 30% increase after the storm. This increase was spatially variable, much larger nearshore than offshore. Power spectral analysis of Acoustic Doppler Current Profiler (ADCP) data at a shelf site near the study region reveals strong near‐inertial oscillations (NIOs) in the upper layer, with a period of about 36 h, close to the local inertial period. The NIOs intensified mixing and modified the stratification of the upper layer, inducing uplift of nutrients and Chl a into the mixed layer from below, and leading to surface Chl a increase. The relatively shallow nutricline and thermocline in the continental shelf region before the storm were favorable for upwelling of nutrients and generation of NIOs. Advection of nutrients from enhanced runoff during and after the storm may be responsible for the larger increase of the Chl a nearshore.
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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".