Western Alaska Salmon Stock Identification Program (WASSIP): Cooperation Among Stakeholders to Improve Understanding
Bibliographic record
Abstract
Program (WASSIP) is a unique collaboration among stakeholders and scientists to address long-standing questions about harvest patterns of chum and sockeye salmon in western Alaska fisheries. Born from frustration with widely divergent regulatory decisions based on limited and controversial data, WASSIP created a framework for representatives from affected stakeholders in western Alaska to collectively design a scientific study to address critical information gaps in a highly contentious commercial and subsistence fishing environment. While engaged in the largest salmon genetics study ever conducted (collecting over 325,000 samples), we established a process where representatives of major regional fishery interests accepted responsibility for the design of scientific investigations that would inform regulatory decisions they must live with. Spanning more than eight years, WASSIP analyzed more than 225,000 tissues to determine stock-specific compositions, harvests, and harvest rates of sockeye and chum salmon in subsistence and commercial fisheries across a vast region of coastal western Alaska, including state-managed marine and inshore waters on both sides of the Alaska Peninsula, Bristol Bay, the lower portions of the Yukon and Kuskokwim River drainages, Norton Sound, up around the east side of the
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".