Report of the 6th Annual Ropeless Consortium Meeting: continued development and policy impact of on-demand fishing to prevent large whale entanglements
Bibliographic record
Abstract
Seasonally closed trap fishery areas mitigate large whale entanglement risk. Acoustic retrieval of bottom traps without persistent vertical lines can restore fishery access. Concerns include functionality, cost and operational constraints of acoustically triggered ‘on-demand’ buoyant bottom-stowed line or an inflatable lift bags. Without surface gear attached to a vertical line from the trap(s), virtual gear marking and on-demand gear interoperability remain concerns. U.S.A. east coast lobster and west coast crab, as well as Canadian snow crab have been harvested using on-demand gear in areas otherwise seasonally closed. U.S.A. South Atlantic black sea bass fishery regulations reopened closed areas to on-demand systems. Challenges include bottom gear location estimation and minimizing gear conflict with fixed and mobile gear fisheries. Enforcement solutions include development of a single deck box triggering multiple brands of ondemand gear, and adoption of interoperable acoustic communication standards. Satellite or cellular communication of gear positions between interested vessels must be interoperable between brands of on-demand gear and navigational systems. Policies by which position data will be shared between different user groups are also under discussion. All these facets must be integrated into a regulatory framework in both the U.S.A., and Canada towards sustainability for fisheries and whales glob
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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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.011 |
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".