Increasing value through gear flexibility: a case study of US west coast sablefish
Bibliographic record
Abstract
This paper explores the potential economic gains of allowing additional flexibility in gear choice, within rights-based management programs. A case study of US west coast sablefish (Anoplopoma fimbria) provides an example of a commercially important species where gear-switching is currently occurring within the individual fishing quota (IFQ) program, allowing us to isolate the economic potential of gear flexibility along two important margins: size and quality. We conduct a hedonic price analysis of ex-vessel prices using panel fish ticket data and linear mixed-effect econometric models to examine the influences of gear, size, condition, fishing sector, port group, landing month, and year on the price of sablefish. We generate a counter-factual scenario that represents the IFQ fishery where the use of fixed gear is prohibited by predicting what the size composition of catch would have been if the sablefish had been caught with trawl gear. We find that the flexibility of targeting sablefish with fixed gear between 2011 and 2016 generated an annual mean 10.45% increase in total revenue, or US$1.17 million, compared with the trawl-only scenario. These results show sablefish value increases through implementing gear flexibility, which contributes to a broader conversation of allocative efficiency.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".