Application of <scp>AIS</scp> ‐ and flyover‐based methods to monitor illegal and legal fishing in Canada's Pacific marine conservation areas
Bibliographic record
Abstract
Abstract New approaches are required to undertake the substantial task of monitoring ongoing fishing activity in marine conservation areas to ensure conservation goals are achieved. To address this need, we applied previously developed, yet currently underused, vessel tracking methods based on Automatic Identification System (AIS) and aerial surveillance (“flyovers”) to Canada's Pacific marine conservation areas from 2012 to 2019. We used satellite and terrestrial‐based AIS receivers and flyover‐based visual observations to estimate illegal and legal fishing activity after 185 conservation area (CA) enactments (i.e., static, geographically defined areas with fishing regulations). We compared the effectiveness in detecting fishing activity between the AIS‐ and flyover‐based methods, and used the latter to determine that 93% of vessels were actively fishing in CAs without AIS. The AIS‐based method still detected 3303 h of fishing in CAs after enactment, and both methods estimated 22%–24% of fishing activity in CAs was illegal. The application of these methods also shed light on the complexity of fishing regulations across CAs (i.e., varying and CA‐specific restrictions). This highlighted the need to better align vessel tracking fishing gear classifications with CA regulation specifications, and conversely to simplify regulations (e.g., no‐take), for more accurate monitoring and evaluation moving forward.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".