Risk and information use in two competing fleets: Russian and Cuban exploitation of silver hake (<i>Merluccius bilinearis</i>)
Bibliographic record
Abstract
Information exchange should influence the ability of individual vessels to exploit fish aggregations, ultimately influencing fishing efficiency. We examined this hypothesis using data from Cuban and Russian vessels pursuing silver hake (Merluccius bilinearis) on the Scotian Shelf from 1989 to 1993. Cuban fleet size and organization were similar among the years, while the Russian fleet decreased in size and became profit driven during this time. Changing fish abundances prevent direct comparisons of fishing success between years, but the relative performance of the nations provided a basis for interannual comparison of fishing success. The risk of gear damage during a trawl differed between the nations in the years studied. From 1989 to 1992, vessel performance improved after a move of over 20 nautical miles, but this trend was absent from the 1993 data. When movements were separated into potential tactics, moving to an area where other ships were fishing was most common, suggesting the use of public information. Russian vessels were significantly less mobile than Cubans immediately following the dissolution of the Soviet Union and their seasonal catch rates were typically lower. In the following year, Russians were more mobile than Cubans and their seasonal performance was comparable.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".