Freshwater Fisheries in Canada: Historical and Contemporary Perspectives on the Resources and Their Management
Bibliographic record
Abstract
Abstract.—The number of nonnative freshwater fish species introduced into, and within, Canada has increased over time. Twenty-one exotic species, not native to Canada, are now established in Canada and were historically introduced through legal stocking. More recently, several species were introduced through ballast water, a vector that now appears to be effectively regulated, and species illegally released into the wild from the live food trade appear to be the emerging primary vector of introduction of exotic species. The primary impacts of exotic species are competition with and, to a lesser degree, predation on or hybridization with, native species. At least 26 species native to Canada have become introduced and established beyond their native range. These human-mediated range expansions of domestic nonnative species were the result of legal stocking, which no longer legally occurs. However, illegal stocking continues to be an ongoing problem. Most domestic nonnative species compete with native species but, unlike exotic species, many also predate on native species. Under Canada’s national aquatic invasive species (AIS) regulations, only four species are prohibited from importation nationally and the introduction of 26 domestic species is prohibited beyond their native range. Five provinces have AIS regulations that include prohibition of some exotic and domestic nonnative freshwater fish species. The ongoing introduction, establishment, and spread of both exotic and domestic nonnative freshwater fish species across the country is an indication that existing AIS regulations are not working. Unless AIS regulations are more effectively implemented, the introduction of exotic and domestic nonnative species will continue and likely be exacerbated by climate change.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".