Testing and Refining the Assumptions of Put-and-Take Rainbow Trout Fisheries in Alberta
Bibliographic record
Abstract
Stocking catchable-size trout to create sport fisheries is based on a simple conceptual model: stocking more fish creates better fisheries that attract more anglers. Organizations typically stock variable densities of fish (i.e., cost) and expect correlated responses in catch rate and angler effort (i.e., benefit). We tested the assumptions inherent in stocked rainbow trout fisheries in Alberta and found no correlation between these costs and benefits of stocking. Rather, stocking low or high densities of trout created low-density stocks that supported low-catch-rate fisheries, but attracted many anglers if catch rates exceeded 0.08 trout/angler-hr and lakes were close to anglers’ homes. We propose a fiscally responsible model of stocking (i.e., stock minimum numbers of fish to remain above an optimal catch rate at lakes selected to attract anglers) that allows managers to either increase stocking sites or reduce stocking costs while maintaining angler effort.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".