Bibliographic record
Abstract
1. Unreported catches are a concern in fisheries management 2. Underreporting and illegal fishing threaten conservation 3. Measures taken to restrict legitimate fisheries in response to declines in stocks can be nullified by unaccounted fishing mortality 4. Socio-economic losses can occur 1. Sources of unreported catch It is illegal to retain salmon caught in gear directed at other species (applies to marine, estuary and fresh water) Unreported catches can occur in a multitude of small, localized fisheries taking place over a very broad geographic expanse (upwards of 700 rivers in eastern Canada and 10 000 km’s of coastline) Some of these fisheries are illegal but some underreporting occurs in legal recreational and aboriginal fisheries It is difficult to quantify the unreported catches as they are considered to result mainly from illegal fishing activities 2. Methods used to estimate unreported catch In the past, Fishery Officers estimated illegal catches and underreporting in legal fisheries based on local knowledge Surveys of river, estuarine and coastal areas by Fishery Officers for illegal fishing activities combined with local knowledge of the extent of illegal activities were used to estimate the total illegal catch Frequently, because of a lack of information, unreported catch values have been carried forward from previous years
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.110 | 0.044 |
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".