Effectiveness of shore‐based remote camera monitoring for quantifying recreational fisher compliance in marine conservation areas
Bibliographic record
Abstract
Abstract Marine conservation areas require high levels of compliance to meet conservation objectives, yet little research has assessed compliance quantitatively, especially for recreational fishers. Recreational fishers take 12% of global annual fish catches. With millions of people fishing from small boats, this fishing sector is hard to monitor, making accurate quantification of non‐compliance an urgent research priority. Shore‐based remote camera monitoring was tested for quantifying recreational non‐compliance in near‐shore, coastal rockfish conservation areas (RCAs) in the Salish Sea, Canada. Six high definition trail cameras were used to monitor 42 locations between July and August 2014. Seventy‐nine percent of monitored conservation area sites showed confirmed or probable fishing activity, with no significant difference in fishing effort inside and outside RCAs. Mixed effects generalized linear models were used to test environmental and geographic factors influencing compliance. Sites with greater depth had significantly higher fishing effort, which may imply high, barotrauma‐induced, rockfish mortality in RCA sites. Non‐compliance estimates were similar to aerial fly‐over compliance data from 2011, suggesting that trail camera monitoring may be an accurate and affordable alternative method of assessing non‐compliance in coastal conservation areas, especially for community‐based organizations wishing to monitor local waters. Widespread non‐compliance could compromise the ability of RCAs to protect and rebuild rockfish populations. Increased education, signage, and enforcement is likely to improve compliance.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".