Impacts of anthropogenic inputs on hypoxia and oxygen dynamics in the Pearl River estuary
Bibliographic record
Abstract
In summer, the Pearl River estuary (PRE) experiences hypoxia, largely driven by the high input of fresh water with low dissolved oxygen (DO), abundant nutrients and particulate organic carbon from the Pearl River network. In this study, we used a well-validated physical–biogeochemical model together with a DO species-tracing method to study the responses of hypoxia and oxygen dynamics to the anthropogenic perturbations of riverine inputs (i.e. DO, nutrients, and particulate organic carbon) in July–August 2006. Model results showed that hypoxia in the PRE was most sensitive to riverine inputs of particulate organic carbon, followed by DO concentrations and nutrients. Specifically, a 50 % decrease (increase) in riverine input of particulate organic carbon led to a 47 % decrease (64 % increase) in hypoxic area, with the sediment oxygen demand and water column production being the two most important processes contributing to changes in DO concentration. Changes in the riverine inputs of DO and nutrients had little impact on the simulated hypoxia because of the buffering effects of re-aeration (DO fluxes across the air–sea interface); i.e. the re-aeration responded to the changes in surface apparent oxygen utilization (AOU) associated with river-induced variations of oxygen source and sink processes. The PRE features shallow waters (with averaged depth of 10 m) in which oxygen provided by the re-aeration could penetrate to bottom waters via vertical diffusion and largely offset the changes in DO contributed by other oxygen source and sink processes. This study highlights the importance of re-aeration in reducing hypoxia variability in shallow estuaries.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".