Balancing boat‐electrofishing sampling effort against costs for nearshore fish communities in the Toronto waterfront, Lake Ontario
Bibliographic record
Abstract
Abstract To enhance management of aquatic systems, long‐term monitoring programs are crucial. However, managers often lack sufficient guidance in decision‐making. In this study, we analyzed nearshore electrofishing data from Lake Ontario spanning 18 years and over 100,000 caught fish to assess sampling designs for various coastal habitats. Using simulation, we evaluated precision of catch per unit effort (CPUE) for all species, piscivores, and specialist species, at different levels of electrofishing sampling effort. For overall CPUE in any habitat type, increased precision declined with additional electrofishing runs, particularly after reaching 30–40 runs. Adjustments in sampling effort, such as adding 10 runs per year for open‐coast sites, increased precision of overall CPUE by 10%. Adding 8 runs per year for wetlands increased precision of specialist‐species CPUE by 10%. However, additional runs in embayments did not increase precision for all species, piscivores, or specialist species. Our findings underscore the importance of considering community composition and abundance when evaluating CPUE precision and illustrates a flexible approach to optimize sampling effort in aquatic monitoring programs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".