Microplastics in the Salish Sea: A wholistic approach
Bibliographic record
Abstract
Plastic production has increased globally at an exponential rate and with it so has marine plastic pollution. The Salish Sea is home to many ecologically and economically important species as well as substantial urban populations, making it one of the most interesting and important locations to study regional microplastic sources and contamination. The field of marine microplastics is rapidly growing, doubling roughly every year; the Salish Sea has been involved in research since 2008, developed NOAA’s marine debris methods. This panel aims to highlight the wholistic approach of marine microplastic research and removal efforts in the Salish Sea through examining how types of samples (sediment, water, organism etc.), spatial-temporal scales, and methodologies, can be utilized and applied to community-driven questions. Here, we discuss the field from multiple perspectives, including academic, NGO, government, policy, BIPOC youth, and businesses from the US and Canada. We will delve into the intricacies of how regional microplastic research goes beyond tradition published and peer reviewed scientific papers, often including work done with community members, volunteer groups, students, and activists. We are committed to reducing marine plastic pollution and believe that working together through a combination of science, policy, and public awareness are necessary.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".