Freshwater Fisheries in Canada: Historical and Contemporary Perspectives on the Resources and Their Management
Bibliographic record
Abstract
Abstract.—Canada is a country rich in natural resources. Given the importance of both resource extraction to Canada’s economy and freshwater fishes, I synthesize available information to assess Canada’s ability to monitor the impacts of tailings ponds on freshwater fishes. Using widely available data, I found that current monitoring activities can only assess large effects on freshwater fishes. These results suggest that environmental monitoring may fall victim to the “shifting baseline syndrome,” where contemporary changes to freshwater ecosystems are compared to relatively recent time periods after which impacts may have already occurred. I then use the example of recent tailing pond failures in western Canada, among the worst in North American history, to describe the inherent risk of tailings pond structures. Unlike oil tanker spills, the rates of tailing spills have significantly increased in the past few decades, the majority from faulty infrastructure. With over 1 billion m3 of tailings held in containment systems covering 110 km2 in the oil sands region, I use the Obed and Mount Polley mine spills as cautionary tales of the risk of failing pond infrastructure. Finally, I provide a contemporary perspective on how to improve the monitoring of Canada’s tailings ponds. I highlight the need for consistency in regulatory and monitoring approaches, the need for an engaged citizenry, and the use of fisheries professionals as means of improving environmental monitoring activities.
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 | 0.003 | 0.009 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".