A numerical model study of the main factors contributing to hypoxia and its interannual and short-term variability in the East China Sea
Bibliographic record
Abstract
Abstract. A three-dimensional physical-biological model of the marginal seas of China was used to analyze interannual and intra-seasonal variations in hypoxic conditions and identify the main processes controlling their generation off the Changjiang (or Yangtze River) estuary. The model was compared against available observations and reproduces the observed temporal and spatial variability of physical and biological properties including bottom oxygen. Interannual variations of hypoxic extent in the simulation are partly explained by variations in river discharge but not nutrient load. As riverine inputs of freshwater and nutrients are consistently high, promoting large productivity and subsequent oxygen consumption in the region affected by the river plume, wind forcing is important in modulating interannual and short-term variability. Wind direction is relevant because it determines the spatial extent and distribution of the freshwater plume, which is strongly affected by either upwelling or downwelling conditions. High-wind events can lead to partial reoxygenation of bottom waters and, when occurring in succession throughout the hypoxic season, can effectively suppress the development of hypoxic conditions, thus influencing interannual variability. A model-derived oxygen budget is presented and suggests that sediment oxygen consumption is the dominant oxygen sink below the pycnocline and that advection of oxygen in the bottom waters acts as an oxygen sink in spring but becomes a source during hypoxic conditions in summer, especially in the southern part of the hypoxic region, which is influenced by open-ocean intrusions.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".