Data-limited fisheries methods shed light on the exploitation history and population dynamics of ESA-listed Yelloweye Rockfish in Puget Sound, Washington
Bibliographic record
Abstract
Yelloweye Rockfish in the Puget Sound/Georgia Basin were listed under the Endangered Species Act (ESA) in 2010, and a formal recovery plan for these species was published by NOAA Fisheries in 2017. Under this recovery plan, the criteria for delisting or downlisting are specified as certain levels of spawning potential ratio (SPR), which compares the reproductive capacity of a stock in its current condition relative to an unfished condition. SPR is a proxy for relative stock biomass, a commonly used metric of stock status. Although these metrics can be estimated without catch histories, catch histories improve our understanding of population dynamics over time, a useful addition to monitoring ESA recovery, but historical removal histories were not available for these distinct population segments (DPSs). We therefore reconstructed the catch history from fisheries records and collated length data from contemporary and historical hook-and-line surveys to fit a data-limited version of a statistical catch-at-age model. Small sample sizes and low confidence in the historical catch data translated into large uncertainty intervals in the population dynamics of the species. Despite this uncertainty, the stock assessment model estimated Yelloweye Rockfish is above 25% of unfished biomass (the limit biomass reference for federally managed rockfishes on the Pacific coast) under the assumption of deterministic recruitment. However, in line with recent genetic evidence, the DPS of Yelloweye Rockfish listed under the ESA extends from south Puget Sound to Queen Charlotte Strait in British Columbia. The Canadian portion of this population is currently estimated to be at 32% of unfished biomass (95% quantiles: 15 to 68%). Thus, the disjunction between the biological boundaries of the population and the jurisdictional boundaries between Canada and the U.S. present an additional source of uncertainty in assessing recovery.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".