Reconstruction of Freshwater Fisheries Catches : Canada, Minnesota (USA), and ASEAN Countries
Bibliographic record
Abstract
This Fisheries Centre Research Report presents ‘reconstructions’ of the catch of inland (i.e., freshwater) fisheries for all 10 provinces and 3 territories of Canada, for the U.S. state of Minnesota and for the 10- member countries of the Association of Southeast Asian Nations or ASEAN. These catch reconstructions, like the previous catch reconstruction of the marine fisheries catches of the world’s maritime countries performed by the Sea Around Us (see www.seaaround.us.org) are required because, unfortunately, the catch statistics submitted to the Food and Agriculture Organization of the UN (FAO) by its member countries are incomplete. These ‘official’ statistics do not include discards and usually ignore the catch of subsistence and recreational fisheries, the latter being an issue that is far worse in inland than in marine fisheries. Thus, in Canada, the ‘commercial’ fisheries whose catches are the only statistics reported to the FAO, make up a small fraction of overall catches, which are overwhelmingly dominated by recreational catches, i.e., fish killed for sport. This is the first of a series of which will be a series of Fisheries Centre Research Report by the Sea Around Us which will document the world’s inland fisheries catches. Thus, they will establish a baseline, which jointly with our marine catch reconstruction, will provide a realistic account, also available on our website, of the fish and invertebrates that have been extracted from the global ocean and the Earth’s inland waters. This should improve not only our evaluation of their contribution to our food security and livelihoods, but also of their impact on marine and freshwater biodiversity and animal welfare.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".