Gearing up: Methods for quantifying gear density for fixed-gear commercial fisheries in the U.S. Atlantic
Bibliographic record
Abstract
Fixed-gear commercial fisheries are unique due to their occupancy nature, claiming areas of the ocean for discrete periods. As space conflicts arise from competing ocean uses, there is an increased need to understand and categorize fixed-gear fisheries to incorporate into marine spatial planning (MSP) efforts. We used fishery-dependent data and input from stakeholders to discern fleet dynamics of all gillnet and trap/pot fisheries in U.S. waters of the Northwest Atlantic Ocean. A Fixed-Gear Fishery Layer (FGFL) was developed combining fishery subgroups that were categorized around gear type, gear configuration, and species landed. Fishing effort from each subgroup was spatially allocated onto a 1 nm2 (1.9 km2) grid using methods that relied on the level of detail available from trip reporting and monitoring, each with differing degrees of spatial resolution. This stepwise process allowed trips reported with minimal spatial detail to be included while not compromising trips where greater spatial precision existed. We demonstrate how the FGFL has been used for two MSP projects focused on protected species conservation and wind energy development.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".