Visual modeling approach to assess fishing gear efficiency and visual capacity of snow crab (Chionoecetes opilio): a case study on luminescent-netting pots in commercial fisheries
Bibliographic record
Abstract
This thesis explored the visual capabilities of snow crab (Chionoecetes opilio) and their interactions with luminescent-netting pots to better understand the role of light intensity during capture. I compared three experimental luminescent-netting pots with increasing brightness to the traditional non-luminescent pots used in the Newfoundland and Labrador snow crab fishery. First, I developed a visual model for snow crab by characterizing their visual parameters, the emitted pot light, and environmental factors. I found that the distance that snow crab can see the light from the pots at 200 meter depth (fishing grounds) depends primarily on the solar angle (height of the sun) and the time elapsed after deployment. I then performed field trials, comparing the catch per unit effort (CPUE; number of crab per pot) and size-selectivity (size-based capture) between each pot treatment. Results showed that the lowest light level pot performed better in comparison to other treatments when considering a balance between commercial, management, and environmental concerns, catching more large crab and fewer small crab. In conclusion, this thesis describes how snow crab vision and pot light intensity effect luminescent-netting pot effectiveness and that increasing the light in luminescent-netting pots does not lead to an improved approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".