Diving back in time: Extending historical baselines for yelloweye rockfish with Indigenous knowledge
Bibliographic record
Abstract
Abstract Ocean systems, and the culturally and commercially important fishes that inhabit them, face growing threats. Increasingly, unconventional data sources are being used to inform fisheries research and management for data‐poor species. Listed as a species of special concern in Canada, yelloweye rockfish ( Sebastes ruberrimus ) are vulnerable to exploitation, and have historical and cultural value to Indigenous people. In this study, Indigenous fishers of British Columbia, Canada, were interviewed and asked about observed changes to the body sizes (length) and abundance of this species over the last ~60 years, and the factors driving these changes. Their current and historical estimates of size and abundance were compared with current biological survey data. Forty‐two semi‐directed interviews were carried out and 89% of respondents observed a decrease in yelloweye rockfish body sizes since the 1980s. The median historical (1950s–1980s) length was 84 cm, compared with the median modern (2010–2015) length of 46 cm. All but one respondent reported substantial decrease in yelloweye rockfish abundance since their earliest fishing experiences (1950s to1980s, depending on participant's age), with a third suggesting the change was most evident in the early 2000s, followed by the 1980s (21%) and 1990s (17%). Sizes of modern yelloweye rockfish estimated by participants resembled estimates derived from ecological data recorded concurrently at the study region. This study illustrates a repeatable method for using traditional and local knowledge to extend baselines for data‐poor species, and highlights the value of integrating Indigenous knowledge into fisheries research and management.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".