Incorporating spatial and seasonal dimensions in a stock reduction analysis for lower Fraser River white sturgeon (<i>Acipenser transmontanus</i>)
Bibliographic record
Abstract
We applied a spatially and seasonally structured stock reduction analysis (SRA) model to white sturgeon ( Acipenser transmontanus ) in the lower Fraser River, British Columbia, to estimate trends in abundance since the 1800s and evaluate the current status of the population. We used a sequential Bayesian state–space estimation approach to incorporate prior information from other analyses and evaluate the updating of prior knowledge within the SRA model. The estimated ratio of the abundance of spawning fish in 2004 to relative to unfished conditions was slightly higher than estimates from other studies; on average, 27% of the posterior probability was associated with a 2004 spawning stock abundance of 50% or less of the unfished abundance. Estimates of the current abundance of fish vulnerable to the lower Fraser River recreational fishery were higher than those obtained in other recent SRAs that ignored spatial structure. We also performed the analysis using a spatially aggregated version of the SRA model and obtained lower estimates of unfished biomass and depletion and higher estimates of fishing mortality rates compared with the spatially structured model. We evaluated two structural hypotheses about age-specific vulnerabilities in the historical commercial fishery; assumed vulnerabilities had a marked impact on estimated fishing mortality rates.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".