The Bait Industry as a Potential Vector for Alien Crayfish Introductions: Problem Recognition by Fisheries Agencies and a Missouri Evaluation
Bibliographic record
Abstract
Abstract “Bait-bucket introductions” related to the fishing bait industry are the suspected primary cause of alien (non-indigenous) crayfish introductions that have damaged North American aquatic ecosystems. Our 2008 survey of U.S. and Canadian fisheries agencies revealed that 49% of respondents reported aquatic resource problems that were believed to have been caused by bait-bucket introductions of alien crayfishes. Most respondents reported existing regulations designed to address those problems; however, only 4% prohibited the use of live crayfish bait. Our 2002–2007 examination of Missouri bait shops revealed sales of illegal and invasive alien crayfishes by bait shop proprietors who could not identify the species they were selling. Fisheries agencies should consider more effective bait regulations and education to prevent negative impacts to aquatic biodiversity, habitat, and fisheries that can result from alien crayfish introductions.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".