Comment on bg-2021-358
Bibliographic record
Abstract
Abstract. The summertime low-oxygen conditions in the Pearl River Estuary (PRE) have experienced a significant spatial expansion associated with notable deoxygenation in recent decades. Nevertheless, there is still a lack of quantitative data on the long-term trends and interannual variabilities in oxygen conditions in the PRE as well as on the driving factors. Therefore, the long-term deoxygenation in a subregion of the PRE (the coastal waters off Hong Kong) was comprehensively investigated in this study using monthly observations during 1994–2018. To evaluate the changes in scope and intensity of oxygen conditions, an indicator (defined as the low-oxygen index, LOI) that integrates several metrics related to low-oxygen conditions was introduced as the result of a principal component analysis (PCA). Moreover, primary physical and biogeochemical factors controlling the interannual variabilities and long-term trends in oxygen conditions were discerned, and their relative contributions were quantified by multiple regression analysis. Results showed that the regression models explained over 60 % of the interannual variations in LOI. Both the wind speeds and concentrations of dissolved inorganic nitrogen (DIN) played a significant role in determining the interannual variations (by 39 % and 49 %, respectively) and long-term trends (by 39 % and 56 %, respectively) in LOI. Due to the increasing nutrient loads and alterations in physical conditions (e.g., the long-term decreasing trend in wind speeds), coastal eutrophication was exaggerated and massive marine-sourced organic matter was subsequently produced, thereby resulting in an expansion of intensified low-oxygen conditions. The deteriorating eutrophication has also driven a shift in the dominant source of organic matter from terrestrial inputs to in situ primary production, which has probably led to an earlier onset of hypoxia in summer. In summary, the Hong Kong waters have undergone considerable deterioration of low-oxygen conditions driven by substantial changes in anthropogenic eutrophication and external physical factors.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.017 | 0.009 |
| Insufficient payload (model declined to judge) | 0.131 | 0.116 |
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".