in Flemish Cap and Flemish Pass
Bibliographic record
Abstract
We review the fishery and biological investigations carried out in the Flemish Cap and Flemish Pass area during the 25 years of NAFO history. In particular, we examine the information available on the biology and population structure of roughhead grenadier recorded in the following 7 research surveys carried out in the Flemish Cap and Flemish Pass area (NAFO Div. 3LMN): Russian bottom trawl research survey (1974–86), Russian longline research survey (1982), Canadian deepwater bot-tom trawl research survey (1991, 1994 and 1995), European Union longline research survey (1996), European Union Flemish Cap bottom trawl research survey (1988–2004), Canadian autumn bottom trawl research survey (1978–2004) and Spanish 3NO bottom trawl research survey (1995–2004). In addition, biological data collected aboard Spanish commercial fishery vessels were analysed from 1997 to 2004. Indices of biomass from various surveys suggest stability during recent years. Most surveys indicated catch rates as well as average fish size increased with depth. Growth studies by sex demonstrated both sexes grew similarly up to 9–10 years, but the male growth was slower thereafter. Estimates of size and age at 50 % maturity and fecundity were very similar for the different data sets studied, showing a late maturity and low fecundity. All the studies examined found that the roughhead grenadier show a very wide feeding spectrum.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".