Fishing power increases from technological development in the Faroe Islands longline fishery
Bibliographic record
Abstract
During an effort-regulated period from 1996 to 2002, unregistered annual increases of 0.3% of hooks fished per day were demonstrated for the Faroe Islands longline fishery. However, annual increases were higher (1.5%) during a preceding total allowable catch regulated period, thereby invalidating an expectation of the 1996 shift in regulations (output to input control) to have induced increases in the number of hooks set per day. Underlying this result is a substantial increase in total yearly effort (fishing days) and a shift in targeting behaviour from secondary to primary (high value) target species in response to the transition from output to input control. Interview data on technology were combined with logbook data and analysed with generalized linear modelling to demonstrate haddock ( Melanogrammus aeglefinus ) and Atlantic cod ( Gadus morhua ) catch-per-unit-effort increases of 51% and 26%, respectively, following the introduction of skewed hooks and swivel line. The technological introductions were not correlated to regulation shifts. So, rather than the management system in force, an ongoing technological development seems to be the principal driver of fishing power trends. The results highlight the need to explicitly address technological development and targeting behaviour when attempting to meet conservation objectives through input control of fisheries.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".