RECRUITMENT PATTERNS AND PRECAUTIONARY EXPLOITATION RATES FOR GEODUCK (PANOPEA ABRUPTA) POPULATIONS IN BRITISH COLUMBIA
Bibliographic record
Abstract
An age-structured projection model was used to study the impacts of alternative exploitation intensities on geoduck populations, based on a large accumulation of survey data, age composition data and published estimates of natural mortality. Data were analyzed and results presented by geographic region. Historic recruitment patterns were back calculated using an age-structured model. Trends in recruitment were independent of the value of M used, although rates were higher when M of 0.036 was applied compared with M of 0.016. Historic recruitment rates were found to be highly variable in the prefishery state: rates generally increased from the early 1930s to 1950, decreased until early 1960, increased to another peak in the mid 1960s, declined until the mid 1980s and have been increasing to present. Future recruitment was simulated from the estimated historic pattern. The fishing patterns examined were combinations of different mortality rates (0.016 or 0.036) and different periods of historical recruitment (beginning in 1940 or 1960). For each simulation year, in each set of 1,000 runs, the ratio of current biomass to virgin biomass was calculated and compared with the management objective of not exceeding 50% of virgin biomass within 50 y of harvest. An exploitation rate of 1.2% and 1.8% of estimated current biomass is recommended for the west coast of Vancouver Island and the rest of the coast respectively.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".