A numerical model study of the main factors contributing to hypoxia and its sub-seasonal to interannual variability off the Changjiang Estuary
Bibliographic record
Abstract
Abstract. A three-dimensional physical-biological model of marginal seas of China was used to analyze variations in hypoxic conditions and identify the main processes controlling their generation off the Changjiang Estuary. The model was validated against available observations and reproduces the observed temporal and spatial variability of hypoxia. Dissolved oxygen concentrations undergo a seasonal cycle, with minima generally occurring in August or September, and vary latitudinally with a longer duration of low-oxygen concentrations in the southern part of the hypoxic region. Interannual variations of hypoxic extent are primarily associated with variations in river discharge and wind forcing, with high river discharge promoting hypoxia generation. At synoptic time scales, strong wind events (e.g. typhoons) can disrupt hypoxic conditions. During the oxygen-depleted period (March–August), air–sea exchange acts as an oxygen sink in oversaturated surface waters. In the subsurface, biological oxygen consumption tends to dominate, but lateral physical transport of oxygen can be comparable during hypoxic conditions. Oxygen consumption in the water column exceeds that of the sediment when integrated over the whole water column, but sediment consumption is dominant below the pycnocline. Vertical diffusion of oxygen acts as the primary oxygen source below the pycnocline and shows a seasonal cycle similar to that of primary production. 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".