Evaluating applications of shore-based camera monitoring to improve estimates of effort, retention, and compliance of recreational salmon fisheries
Bibliographic record
Abstract
In addition to being primary targets of recreational fisheries, Pacific salmon provide key ecosystem value in British Columbia, Canada. While data on commercial fisheries are historically well-researched, less is known about recreational fisheries and associated mortality. The purpose of this work is to demonstrate how autonomous shore-based cameras can integrate into, and improve upon, existing creel survey methods while providing new information related to recreational salmon fishery dynamics. Imagery from three locations in Sooke, British Columbia were used to estimate effort and retention by integrating creel survey data, and to improve understanding of compliance to a seasonal area-based fishery closure through two separate empirical studies. The approach here utilizes high-capture rates, allowing for robust temporal resolution, while integrating efficient data processing methods through a novel two-step image annotation process that was developed for the analysis of over 1.5 million images. The results from various temporal analyses suggest that cameras can substantially aid in improving the understanding of daily fishing patterns, while also providing validation for the optimal timing of existing creel surveys. Noncompliant fishing during a seasonal spatial closure was also notable and provides the first known study of salmon fishery compliance using cameras in British Columbia. Lastly, evidence was provided supporting substantial off-season fishing effort, which had previously been lacking in the existing monitoring framework. The subsequent recommendations from this work reveal that this monitoring approach could provide immediate and tangible benefits for improving recreational salmon fishing monitoring in British Columbia. Beyond, the approach that was developed can be applied at broader scales and could benefit managers with tools that can support camera monitoring of recreational fisheries to foster a greater understanding of stressors that impact vulnerable and at-risk fish species.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".