Report of the Data Deficiency Coordination Meeting with the RACs (WKDDRAC)
Bibliographic record
Abstract
Within the North West Waters RAC and the North Sea RAC there has been mounting concern that data deficiencies of various kinds impair the quality of an increasing number of ICES stock assessments. The purpose of the WKDDRAC meeting was to discuss a proposal prepared jointly by the North West Waters and North Sea RACs that regional task forces, involving fisheries scientists, fisheries managers and fishermen be formed to identify those fisheries suffering from data deficiencies, examine the nature of those deficiencies and set in motion remedial measures that would over time improve the situation. See Annex 1. Participants included the Chief Executive of the National Federation of Fishermen’s Organisations (Barrie Deas), also chair of the Demersal Working Group of the North Sea RAC, a Fishery Policy Officer from the Scottish Fishermen’s Federation (Rory Campbell) also member of the NSRAC demersal WG, the chairs of relevant ICES as-sessment working groups (North Sea (Clara Ulrich), Celtic Sea (Joel Vigneau), and hake, megrim and monkfish (Carmen Fernandez)), the Danish data coordinator (Jørgen Dalskov), one of the ACOM vice-chairs (Manuela Azevedo), the Head of the ICES Advisory Services (Poul Degnbol), a Professional Secretary familiar with data issues (Barbara Schoute) and the chair of ACOM (J.-J. Maguire). For participants’ list, see Annex 2.
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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 | 0.050 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.090 | 0.034 |
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".