Features of pollock fishing technology
Bibliographic record
Abstract
The purpose of the work is to study the features of specialized pollock fishing in the North Pacific Ocean. Identification of ways to increase the selectivity of specialized pollock fishing. Methods used: features of specialized pollock fishing in the North Pacific Ocean were investigated by analyzing regulatory documents governing fishing in Russia, the USA and Canada. The study of the selectivity of specialized pollock fishing and ways to increase it was carried out by analyzing the results of experimental work on the pollock fishing. Novelty: materials were obtained on the selectivity of specialized pollock fishing by Russian fishermen. Methods are proposed to increase selectivity of specialized pollock fishery. Result: non-compliance of trawl bag parameters with the requirements of fishing rules in the Far Eastern fishery basin for by-catch of fish of non-mental length was revealed. Practical significance: the obtained materials made it possible to assert that for the extraction (catch) of pollock in the North Pacific Ocean, which meets the requirements of fishing rules in the Far Eastern Fisheries Basin, there are a number of ways: an increase in the mesh size in a trawl bag to 120 mm or more, use in a trawl bag and with the landing of “T90,” use in a trawl bag flexible selective devices.
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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".