Predicting the Ecotoxicological Impacts of Microplastics in the Salish Sea using GIS
Bibliographic record
Abstract
Microplastics are ubiquitous in the world's oceans and have negatively impacted marine biota and ecosystem health. The Salish Sea, an inland sea ranging from Vancouver to Puget Sound, is an ecologically significant ecosystem. This study determined the areas in the Northern Salish Sea (Canadian jurisdiction) in which microplastics are likely to accumulate and subsequently where they are likely to cause ecological harm. Modelling and weighted raster analysis was performed using Geographic Information Systems (GIS). Areas of highest risk were identified, four key ecological areas of concern in relation to the results were investigated, and the potential impacts of microplastics on two key sensitive species (southern resident killer whales and Chinook salmon) were discussed. By identifying vulnerable areas and where microplastics are likely to accumulate, the results could be helpful for conservation managers, fisheries management, and natural resource managers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".