Salish Sea Marine Survival Project: Putting Findings into Action for the Future of Salmon
Bibliographic record
Abstract
The Salish Sea Marine Survival Project (SSMSP, https://marinesurvivalproject.com) published its final Synthesis Report in 2021, summarizing key findings and recommendations from over 5 years of research into the causes of poor marine survival for Salish Sea Chinook, coho, and steelhead salmon. The project, led by Long Live the Kings in the U.S. and the Pacific Salmon Foundation in Canada, united more than sixty regional and international partners to enable one of the most comprehensive assessments of the Salish Sea ecosystem. It offers a critically important model for large-scale scientific collaborations addressing systemic, transboundary questions. This presentation summarizes the SSMSP’s approach and core findings about the drivers of salmon and steelhead productivity in the Salish Sea: specifically, climate-change related shifts at the base of the marine food web, increased numbers of predators, and the local effects of pollution, disease, and habitat loss. The presentation will also look ahead to ongoing studies, ecosystem modeling work, research and monitoring programs and needs for continued coordination among regional partners, and recommended management strategies based on the SSPMSP’s findings.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".