The influence of human population change and aquatic invasive species establishment on future recreational fishing activities to the Canadian portion of the Laurentian Great Lakes
Bibliographic record
Abstract
We project how human population change (2018 to 2046) and aquatic invasive species (AIS) establishment events of bigheaded carps (Hypophthalmichthys spp.) and grass carp (Ctenopharyngodon idella) might combine to affect future Canadian recreational fishing activity for the Laurentian Great Lakes. Human population change is expected to affect the total number of fishing trips (increase of about 143 000 trips or 11.4%) more than any of the AIS establishment events (maximum decrease of about 44 000 trips or 3.5%). The projected 11.4% increase to the number of fishing trips from human population change, however, lags the 38% projected increase to Ontario, Canada’s population from 2018 to 2046. Increasing urbanization and an aging population, which are associated with reduced rates of fishing participation, were responsible for this difference. The combined effects of human population change and AIS establishment illustrate the importance of accounting for human population change as it reverses the conclusions and results in a projected net increase of between 92 000 and 125 000 in the number of fishing trips. The combined model also identifies potential growth areas for fishing such as shore fishing by urbanites on the western portion of Lake Ontario.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".