Abundance of Skeena River Chum Salmon during the Early Rise of Commercial Fishing
Bibliographic record
Abstract
Abstract We used reported commercial catch data and historical information to estimate the abundance of Skeena River Chum Salmon Oncorhynchus keta during the early rise (1916–1919) in the commercial fishery to provide historical perspective for recovery plans. We applied a Bayesian analysis to address the uncertainties associated with the estimation process. Based on the historical catch of 204,000 in 1919 and an estimated harvest rate of 0.32–0.58, the estimated return of Skeena Chum Salmon ranged from 355,000 to 619,000, with the most probable single estimate being 431,000. The estimated return of Chum Salmon based on the 1916–1919 geometric mean catch of 154,000 ranged from 268,000 to 471,000, with the most probable single estimate being 325,000. Our posterior modal historical estimates are 8–11 times larger than the estimates for the contemporary period 1982–2010 and 39–52 times larger than those for the most recent period of 2007–2010. Intense harvest pressure is the single most probable factor explaining the sustained decline in Chum Salmon abundance, but other interactive factors, notably natural variations in survival, the loss of spawning and rearing habitat, and poor data quality, also are important considerations. Nonetheless, the Skeena catchment is largely pristine today, and our robust estimates of historical abundance should be of value to contemporary management and conservation agencies for the rebuilding of such severely diminished populations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".