The importance of fleet definition for estimating economic exposure of the summer flounder fishery to offshore wind farms
Bibliographic record
Abstract
Abstract As offshore wind development continues across the globe, accurate spatial data are required to characterize fishing activity, inform wind farm siting decisions, and estimate economic exposure. We assess the influence of fishing behavior and fleet definition within a multispecies fishery on coarse (logbook-based) footprint biases using a precise (GPS-based) approach. We constructed precise footprints for 838 trips that caught summer flounder (Paralichthys dentatus) trips and 1439 trips that caught any species in the Summer Flounder, Scup (Stenotomus chrysops), and Black Sea Bass (Centropristis striata) Fishery Management Plan from 2016 to 2021. Using the precise footprints as a ground truth, we compared the intersections and estimated economic exposure between coarse footprints (restricted to the 90th, 75th, 50th, and 25th percentiles) for 37 wind farms in the northeast USA. Unrestricted coarse footprints (90th percentile) consistently identified all “true” intersections with wind farms while also overestimating economic exposure. For the multispecies fisheries, restricting footprints between 25th and 50th percentile yielded the most accurate estimates of economic exposure. This contrasts previous work that found the 25th percentile was most accurate for the targeted longfin squid (Doryteuthis pealeii) fishery, highlighting the importance of fleet definition in this process. Replicating this approach for other fisheries will allow development of a tool to accurately estimate economic exposure by restricting coarse footprints in the absence of fine-scale data.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".