Bull Trout Population Responses to Reductions in Angler Effort and Retention Limits
Bibliographic record
Abstract
Abstract We compared historical (1977–1980) and recent (1997–2001) abundance, catch-per-unit-effort (CPUE), and growth data to assess whether the implementation of restrictive sportfishing regulatory regimes in the 1990s led to changes in abundance and population structure of bull trout Salvelinus confluentus in two small Rocky Mountain lakes in Alberta, Canada. For remote Harrison Lake, we used changes in gill-net CPUE to infer a fivefold increase in bull trout abundance after closure of an access road and implementation of catch-and-release (CR) regulations. Bull trout growth rates decreased as their abundance increased. All large (fork length > 420 mm), old (age >12) bull trout were eliminated after regulatory changes were imposed. Reductions in prey abundance and size as bull trout abundance increased probably contributed to the demise of the large bull trout. For road-accessible Osprey Lake, no change in bull trout mark–recapture abundance estimates or growth rates were observed despite implementation of CR regulations and, later, closure of the lake to angling. We speculate that illegal angling and migration of bull trout between Osprey Lake and its tributary streams limited abundance responses at this site. A variety of site-specific factors, including ease of access, angler noncompliance with regulations, and local metapopulation structure of bull trout, led to variable bull trout population responses to the implementation of restrictive angling regulations. Active advertisement and enforcement of regulations may be required to achieve increased bull trout abundance at small, easily accessible waterbodies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".