Surviving Fisheries Management: Aquaculture, Angling, and Lake Ahmic
Bibliographic record
Abstract
The vast majority of inland waters in Ontario have been designated as purely recreational fisheries. Environmental historians who study human-fish relations have demonstrated the influence of anglers in the establishment of fishing regulations and fisheries management policies that sought to maximize fish resources for sport fishing and fishing tourism. To achieve this goal, aquaculture programs were conducted throughout Ontario that artificially reared fish and planted them in lakes. For over a century, from approximately 1860-1960, Ontario relied on aquaculture as a blanket solution to all fishery problems. Over the past fifty years, fisheries science has questioned the ecological benefits of stocking programs. Stocking efforts in the province have been drastically reduced since the 1960s but have continued largely because of grass root initiatives from concerned anglers. Lake Ahmic is home to a small cottage community based out of the village of Magnetawan. The lake has been stocked with a variety of fish species for over a hundred years. In addition to this, several species have been accidentally introduced to Lake Ahmic altering its ecological balance. Between 1987 and 2006, a local angling organization was responsible for initiating and running a walleye-stocking program on Lake Ahmic. In 2006, to the disappointment of the local anglers and greater Magnetawan community, the Ontario Ministry of Natural Resources canceled the stocking program. At the root of the discord between the community and the government is a century long history of efforts to engineer a desirable nature at Lake Ahmic, as well as shifting ideas of what this desirable nature is, and the role that science should play in bringing it about. I argue that a century of stocking fish on Lake Ahmic has reified the practice into the community’s conservation ethos. The environmental history of Lake Ahmic adds insight into the social and political tensions that have arisen as a result of the cancelation of the stocking program.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".