Evaluation of spatiotemporal imputations for fishing catch rate standardization
Bibliographic record
Abstract
As commercial fishing activity shifts to target different grounds over time, spatial gaps can be created in catch rate data, leading to biases in derived indices of fish abundance. Imputation has been shown to reduce such biases. In this study, the relative performance of several imputation methods was assessed using simulated catch rate data sets. Simulations were carried out for three fish stocks targeted by a commercial hook-and-line fishery off the southwestern coast of Australia: snapper (Chrysophrys auratus), West Australian dhufish (Glaucosoma hebraicum), and baldchin groper (Choerodon rubescens). For high-growth scenarios, the mean squared errors (MSEs) of geometric and linear imputations were lower, indicating higher accuracy and precision than that for base method (constant value) imputations. For low-growth scenarios, the lowest MSEs were achieved for base method imputations. However, for the final standardized and imputed abundance indices, the base method index consistently demonstrated the largest biases. Our results demonstrate the importance of selecting an appropriate imputation method when standardizing catch rates from a commercial fishery that has changed its spatial pattern of fishing over time.
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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 | 0.077 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".