A Weight of Evidence approach to support the assessment of the quality of Manila clam farming sites in a coastal lagoon
Bibliographic record
Abstract
Aquaculture productivity in coastal lagoons is endangered by a complex interplay of anthropogenic and environmental factors, amplified by the effects of climate change in these sensitive areas. To reach a more comprehensive assessment of farming sites quality, a quantitative Weight of Evidence approach (QWoE) is applied for the first time to data collected at four Manila clam (R. philippinarum) farming sites in the Venice lagoon (Italy). This included sediment quality, chemical bioaccumulation, and biological responses. Results revealed a greater hazard for sites closer to the open sea. In these areas, the combination of sediment characteristics and a higher frequency of salinity and temperature stress could explain the alterations measured at a transcriptional and biomarker level. The findings demonstrate that a QWoE approach that integrates multiple sources of evidence should also include physicochemical conditions in order to better understand the impacts of human activities and other stressors on clam aquaculture productivity.
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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 | 0.141 | 0.360 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.053 | 0.017 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".