Artificial Light in Commercial Industrialized Fishing Applications: A Review
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
Fishing with an artificial light stimulus has existed for thousands of years. It started with simple techniques such as burning a large fire on the beach to attract fish, but over the centuries it has become increasingly technologically advanced. Today, the use of artificial light in commercial fishing plays a very important role in contributing to the total catch yield and economy of many industrialized fisheries. In most cases, fishing vessels employ lights at the surface, but more recently, low-powered LED lights installed directly on fishing gear have also become common. Using artificial light in commercial fishing applications appears to produce various outcomes and trade-offs (i.e., positive and negative effects). Positive benefits can include increases in catch rate, reductions in bycatch, and savings in energy, while negative effects can include ecological costs, overfishing, increased bycatch, production of plastic and marine litter, and greenhouse gas emission. This review provides an overview of fish vision in aquatic animals and the use of light in commercial industrialized fisheries, and provides discussion on potential solutions that strengthen the positive effects and minimize the negative effects of using artificial light in fishing applications.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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