Emerging live sonar technologies in freshwater recreational fisheries: Issues and opportunities
Bibliographic record
Abstract
Abstract Debate about the potential benefits and risks of live sonar technology (also known as live imaging sonar and forward-facing sonar) in freshwater recreational fisheries includes growing discussions regarding regulation. Synthesizing sparse literature, experiences of the coauthors, and observations from traditional and social media, we revealed a varied range of potential outcomes for fisheries when this technology is used. Of particular concern is the ability to find fish that were previously cryptic and to target them in ways that increase capture efficiency (e.g., through snagging where legal or more accurately presenting lures or baits); thus, increasing catchability. Conflicting views within the recreational fishing community about the “fair chase” aspect of this technology have prompted discussions regarding regulations. We anticipate continued debate around this topic and hope that this paper will inspire more empirical research (ecological and human dimensions) to provide resource managers and the recreational fishing community with insights and guidance on how to ensure that live sonar is used in ways that benefit fisheries management and stakeholder interests.
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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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".