Finding fish: grouping and catch-per-unit-effort in the Pacific hake (<i>Merluccius productus</i>) fishery
Bibliographic record
Abstract
Numerous papers have documented the problems in estimating stock abundance using only catch-per-unit-effort (CPUE) data. In this paper, logbook data from the onshore processing sector of the Pacific hake (Merluccius productus) fishery in Oregon from 1992 to 1996 are used to analyze the effect of grouping on CPUE and on search time. Two groups are investigated: (i) simple daily aggregations of vessels on fishing grounds and (ii) yearly information-sharing groups. It is found that both groups are positively associated with CPUE; however, in 1995, this result may be the spurious result of more vessels being drawn into the fishery by good conditions. It is argued that in other years, fishers benefit from grouping by being able to find high-quality patches. Fishers in larger groups also experienced lower search times from 1993 to 1995. However, a pattern of declining effect of information-sharing groups from 1993 to 1996 suggests that fishers who were relatively new to this fishery became less reliant on information-sharing networks for finding patches of fish, although fishers in larger groups still may have benefitted by being able to find higher quality patches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".