Drivers of fuel use in rock lobster fisheries
Bibliographic record
Abstract
Abstract Fuel consumption is a leading cost to fishers and the primary source of greenhouse gas emissions from the global fishing industry. Fuel performance varies substantially between and within fisheries, but the drivers behind this variation are unclear and inconsistent across studies. We surveyed rock lobster fishers in Australia and New Zealand to measure rates of fuel use and assess the influence of technological (e.g. vessel size, engine power), behavioural (e.g. distance travelled, speed), and managerial (e.g. catch per unit effort, fishery capacity) factors. Weighted fuel use intensity across the region was 1,890 l/t. Managerial factors were the most influential drivers of fuel use in single day trips while technological factors heavily influenced multi-day trips. Catch per unit effort was the only significant driver present across both types of fishing trips. The vast majority of surveyed fishers identified fuel use as an important aspect of fishing operations, and nearly half had already implemented changes to try to reduce consumption. Our results suggest that efforts to reduce fuel consumption, costs, and emissions in fisheries need to be tailored to the nature of individual fisheries, as the relative roles of technology, behaviour, and management vary.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".