Responses of phytoplankton communities to the effect of internal wave‐powered upwelling
Bibliographic record
Abstract
Abstract Upwelling and internal waves are known to affect the physical and chemical characteristics of marine ecosystems, yet the processes and mechanisms by which internal waves influence phytoplankton biomass and community composition in upwelling regions are still unclear. In this study, a 72‐h time series of observations was conducted in an upwelling system in the northern South China Sea during the summer of 2014. The results showed that the intensity of upwelling was affected by internal waves, which caused nutrient fluctuations in the upper water column. The phytoplankton total chlorophyll a responded positively to the increase of nutrient concentrations, but only after a time lag of 12–16 h. This overall response was the net result of four different types of responses displayed by nine specific phytoplankton groups. All groups that displayed immediate positive responses continued to respond positively at least 12 h later, whereas all groups that responded negatively showed no time‐lagged responses. Based on the vertical distributions of the nine phytoplankton groups and their known physiological traits, we suggest that these different types of response were the net result of a rapid physical transport effect and a time‐lagged, physiological effect via bottom‐up control.
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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".