Review on Spatial-Temporal Variation of China' Offshore Phytoplankton Chlorophyll and Primary Productivity and Their Variational Mechanism
Bibliographic record
Abstract
Based on synthetical analysis of spatial and temporal distribution and seasonal variations of offshore phytoplankton in different seas of China, and a review on phytoplankton chlorophyll and primary productivity were summarized. Chl-a and primary productivity in different ocean regions present significant spatial and temporal variation under the control of the combined effects of complex physical environmental fields and biogeochemistry: in space, the chlorophyll concentration shows roughly high alongshore regions and low in offshore regions, with the increasing tendency of Chl-a with the increase of latitude in the offshore waters; as for time, it generally showed significant seasonal variation, especially in northern seasonal characteristics, including the maximum of seasonal chlorophyll concentration and productivity in the South China Sea appear generally in the winter, while they in northern waters north China appear gradually in the spring. The main factors regulating the growth of phytoplankton include nutrients, temperature, light, terrestrial input, monsoon, ocean circulation, eddies and so on. However in different ocean regions, limiting factors of phytoplankton growth in different seasons are generally various. In addition, due to the low accuracy of remote sensing and low repeatability and more sparse arrangement of field observation stations, it is difficult to explore the impact of these mechanisms, and thus, the main mechanisms regulating phytoplankton growth are needed for further study in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".