Stock–Yield Model for a Fish with Variable Annual Recruitment
Bibliographic record
Abstract
Abstract Many fish populations have variable annual recruitment, which challenges both population model development and conventional management strategies. Here, we develop a stock– yield model to assess the sustainability of the commercial catch of goldeye Hiodon alosoides from western Lake Athabasca of northeastern Alberta, Canada. Goldeyes have highly irregular annual recruitment; many year-classes are poor and dominant year-classes appear an average of only 1.7 times per decade. In the late 1990s, the mean annual commercial catch from this population was about 11,500 goldeyes—primarily fish from the 1982 and 1989 year-classes. A best-fit stock–yield model developed for this population incorporated (1) the restrictions of the catch from a historic fishery that depleted this population during the 1950s and early 1960s; (2) the restrictions of the known total stock size in the early 1970s, obtained from mark–recapture studies; (3) long-term annual estimates and patterns for stock recruitment (1972–2008); (4) a natural annual mortality rate of 20%; and (5) stock estimates for 1994 obtained from the catchability coefficient (catch per unit effort divided by the magnitude of commercial stock estimated from mark–recapture data for 1972 and 1973). The model suggested that an annual commercial catch of 10,000 goldeyes was sustainable over all years and conditions (1947–2004). This small modeled catch, about 2% of the long-term mean stock number of 540,000 goldeyes, ensured that at least a few thousand mature goldeyes would survive to spawn through periods of poor stock recruitment lasting more than a decade. These results suggest that fish populations with irregular annual recruitment can sustain only a low exploitation rate to prevent stock extinction during long periods of low recruitment.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".