Sportfishing Participation on Lake Ontario: Modeling the Past, Predicting the Future
Bibliographic record
Abstract
Abstract Lake Ontario, one of the five Great Lakes bordering the United States and Canada, has a long, rich tradition of sportfishing. The lake provides a good case study showing how biological, societal, and managerial factors interact with each other to affect fishing participation. Fishing effort at Lake Ontario peaked in 1990 and has since trended downward. Using multiple-regression analysis, we found three variables that explain the number of fishing boat trips taken on Lake Ontario over time. The first variable, time, may be acting as a surrogate for two interacting phenomena: the novelty of Great Lakes salmonid fishing specifically, which appears to be gradually declining, and the declining interest in fishing and outdoor recreation seen nationally. Stakeholders may look first for local explanations for changes, but they need to consider that national and societal trends may be very important. Second, a biological variable, the number of Pacific salmon Oncorhynchus spp. stocked in Lake Ontario, is positively correlated with boat trips. Third, the number of black bass Micropterus spp. harvested is positively correlated with the number of trips. Although not significant at the cutoff level of 0.05, this variable was retained in the model because it was considered an important variable and was close to the cutoff at 0.074. Forecasting these variables into the future, the model predicts a decline of 32% in fishing trips in the ensuing 5 years. Armed with this information, local communities can choose to be proactive and try to counteract the trend predicted by the model. The use of case studies and regression analysis to explain and forecast trends may be applicable to many other fisheries situations across the country.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".