The advantages of an audit over a census approach to the review of video imagery in fishery monitoring
Bibliographic record
Abstract
Abstract Stanley, R. D., McElderry, H., Mawani, T., and Koolman, J. 2011. The advantages of an audit over a census approach to the review of video imagery in fishery monitoring. – ICES Journal of Marine Science, 68: 1621–1627. Technology-based fishery monitoring, or electronic monitoring (EM), has emerged as an alternative to human observers and is being applied in a variety of fisheries. The method records sensor and image data from fishing operations, so can be used to provide 100% monitoring of catches and fishing activity. Alternatively, EM can be used to audit catch data self-reported by harvesters. If the random audit indicates that these data are sufficiently accurate, they can provide useful catch estimates with less reviewing time and, hence, cost. The audit approach was adopted in the groundfish hook-and-line fishery in British Columbia, Canada, in 2006, and experience has shown that it can meet operational requirements for accuracy and timeliness. It is also more robust to the impact of equipment malfunction and can provide an independent estimate of total catch. Moreover, because catch estimates are derived from self-reported data rather than “black-box” records, the estimation process is more transparent and intuitive and, hence, more trusted by harvesters. Although cost reduction is always a primary concern, the audit approach offers significant additional benefits that should be considered in the design and implementation of EM programmes.
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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 | 0.072 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".