Environmentally extended comparison table of large- versus small- and medium-scale fisheries: the case of the Peruvian anchoveta fleet
Bibliographic record
Abstract
Literature on small-scale fisheries usually depicts them as preferable over large-scale–industrial fisheries regarding societal benefits (jobs, jobs per investment) and relative fuel efficiency (e.g., Thomson 1980 ). We propose an environmentally extended Thomson table for comparing the Peruvian anchoveta (Engraulis ringens) fleets of purse seiners, backed up by methodological information and augmented with life cycle assessment (LCA)-based environmental performance information, as a more comprehensive device for comparing fleets competing for the same resource pool. Findings from LCA and a previous study on the anchoveta steel fleet together allowed characterizing the whole Peruvian anchoveta fishery. These results, along with socio-economic indicators, are used to build an environmentally extended Thomson table of the fleet’s main segments: the steel industrial, the wooden industrial, and the wooden small- and medium-scale (SMS) fleets. In contrast with the world figure, the Peruvian SMS fleets show a fuel performance nearly two times worse than the industrial fleets, due to economies of scale of the latter (although the small-scale segment itself (<10 m3) performs similarly to the industrial steel fleet). Furthermore, the absolute number of jobs provided by the industrial fisheries is much larger in Peru than those provided by the SMS fisheries. This is due to the relatively larger development of the industrial fishery, but as in previous studies, the SMS fleets generate more employment per tonne landed than the industrial fleet, as well as more food fish and less discards at sea.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".