The Impact of Increased Accuracy in Geoduck (<i>Panopea generosa</i>) Age Determination on Recommended Exploitation Rates
Bibliographic record
Abstract
Exploitation rates for the Pacific geoduck commercial fishery in British Columbia are currently based on an age-structured model using geoduck age data derived from the ring-counting method. Since 2005, geoduck ages have been determined using the more accurate method of cross-dating. We assessed how the results of age-structured models are impacted by the aging method used by considering 2 data sets that were aged with both methods. Historical recruitment patterns were back-calculated and compared to examine the effect that the aging method had on trends in estimated recruitment over time. Through forward simulation, we examined the influence of alternative fishing intensities on geoduck stocks and evaluated the impact of increased accuracy in age determination on precautionary exploitation rates. Results indicate that the use of the cross-dating methodology has improved our understanding of geoduck recruitment patterns but does not suggest that a change in exploitation rates is warranted. The exploitation rates currently in use are still considered precautionary.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".