Bibliographic record
Abstract
The aim of this paper is to present the status of NAFO roughhead grenadier Subarea 2 and 3 stock. Different assessment methods have been applied based on the available data: XSA, ASPIC and a qualitative assessment based on survey and fishery information. The fit of the data to the XSA and ASPIC has been very poor mainly due to lack of contrast and conflicting information from the surveys data. Therefore the results are not considered representative of the stock situation. \nThere are not available surveys indices covering the total distribution, in depth and area, of this stock. Biomass indices from the surveys with depth coverage till 1400 meters are considered as the best survey information available to monitor trends in resource status because they cover the depth distribution of roughhead grenadier fairly well. \nSurveys biomass indices present a general increasing trend in the period 1995-2004. In the period 2005-2012 all available indices show a clear downward trend except the Canadian Fall (2J+3K) index. In the most recent period (2013-2015) the information of the different indices is contradictory, the Canadian 2J3K and the EU 3L show an increase while EU-FC and EU 3NO continue to decline. With regard to fishing mortality, the trends of the different estimations of F were very similar. F presents a decreasing trend since 1998 till 2006 and since then is more or less stable at very low levels. The recruitment indices analysed (Surveys length and age distributions) show at least three good cohorts: 1993, 2001 and 2012 year classes. To confirm the strength of the last good year class (2012) it would need to have more information about it.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".