Emerging live sonar technologies in freshwater recreational fisheries: Issues and opportunities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it