Report of the ICES/NSCFP Study Group on the Incorporation of Additional Information from the Fishing Industry into Fish Stock Assessments (SGFI)
Bibliographic record
Abstract
The Study Group on Incorporation of Additional Information from the Fishing Industry into Fish Stock Assessments (SGFI) met for two days in The Hague, Netherlands. The participants consisted of fishers, their representatives, scientists employed by the industry, ICES scientists and an ICES representative. The national initiatives to improve the relationships between the fishery and fishery science were presented and discussed in detail. Outstanding were initiatives from Canada, where sentinel surveys are conducted together with the fishery. Very close cooperation was achieved also in surveying and harvesting in the Celtic Sea herring fishery and the Danish discard sampling in the Baltic Sea. As compared with the previous year the overall situation in cooperation has not improved. The relationships have, in a number of cases, become more difficult and information flow from the fishery to the fishery science rather decreased than increased. While this is obvious in a number of countries on the working level between the individual fisher and the scientist, it is, in general, not so much the case on the level of fishery representatives and fishery science.
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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".