Using censored regression when estimating abundance with CPUE data to account for daily catch limits
Bibliographic record
Abstract
In fisheries where there is a limit on total catch in a given period, catch-per-unit-effort (CPUE) data may not be proportional to abundance because catches may be censored at the limit. Commonly used depletion estimators (e.g., Leslie method) could be biased when ordinary least squares (OLS) regression is used to estimate abundance with censored CPUE data. We used simulations to examine the performance of OLS regression and a censored regression approach when estimating abundance and exploitation using censored CPUE data over a range of known exploitation rates. We also applied the censored regression approach to data from a commercial fishery for the eastern oyster (Crassostrea virginica). The censored regression approach always performed better than the OLS regression when estimating abundance and exploitation in our simulations. Harvest and abundance of oysters in Fishing Bay, Maryland, increased during 2009 to 2013 and then decreased through 2016, while exploitation rates had no substantial trend over time. The censored regression approach is useful for estimating abundance and exploitation when the distribution of CPUE is affected by daily catch limits.
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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 | 0.024 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".