Assessing the potential to mitigate climate-related expansion of largemouth bass populations using angler harvest
Bibliographic record
Abstract
Climate-related changes in fish communities can present new challenges for fishery managers who must address declines in cool- and cold-water sportfish while dealing with increased abundance of warm-water sportfish. We used largemouth bass (Micropterus salmoides) in Wisconsin lakes as model populations to determine whether angler harvest provides a realistic method for reducing abundance of a popular warm-water sportfish that has become more prevalent and has prompted management concerns around the globe. Model results indicate largemouth bass will be resilient to increased fishing mortality. Furthermore, high rates of voluntary catch-and-release occurring in most largemouth bass fisheries likely preclude fishing mortality rates required to reduce bass abundance at meaningful levels (≥25% reductions). Increasing fishing mortality in these scenarios may require more “stimulus” than merely providing anglers with greater harvest opportunities via less stringent harvest regulations. Angler harvest could result in populations dominated by small fish, a scenario that may be undesirable to anglers, but could provide ecological benefits in certain situations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".