Cumulative effects assessment of bay ecosystem: Xiamen's Western Sea, a case study
Bibliographic record
Abstract
Considering the deficiency of conventional environment a impacts assessment to protect bay ecosystems from unfavourable impacts of multiple human activities over the years, this paper discusses an effective approach framework to examine addictive or interactive impacts arising from the collective multiple activities of the past. There are three stages of the cumulative effects assessment process: 1) systematically selecting indicators of the ecosystem in question, 2) choosing the quantified or semi-quantified methods to assess indicators of changes, and 3) assessing the past contributions of the activities to the changes. In a case study of Xiamen's Western Sea in China, the indicators were constructed according to their sensitivities to environmental stress and classified into three categories: physical, chemical and biological indicators. The Geography Information System and Pearson correlation analysis were applied to assess changes of the physical and chemical indicators. The case study showed that most of the indicators of Xiamen's Western Sea ecosystem have been changed greatly in the past five decades, especially in shoreline, sea area, water quality, community construction of phytoplankton and benthos, and mangrove forests. Coastal and dike construction and terrestrial pollutant input are the main causes of the changes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".