Report of the Benchmark Workshop on Norway Pout (Trisopterus esmarkii ) in Subarea 4 and Division 3a (North Sea, Skagerrak, and Kattegat). 23–25 August 2016 Copenhagen, Denmark
Bibliographic record
Abstract
The Benchmark Workshop on Norway Pout (WKPout), chaired by External Chair Jerry Ault, USA, and ICES Chair José De Oliveira, UK, met at ICES HQ, Copenhagen on 23–25 August 2016. There were 13 participants, including three external reviewers (two from USA, one from France), Danish, Norwegian and UK scientists, and Danish Industry representatives. The benchmark followed a data evaluation workshop in May 2016 during which input data for the assessment were agreed. The main focus of the benchmark was to agree a new assessment methodology, seasonal SAM (SESAM), for Norway Pout. In addition, reference points and forecasting methodology were discussed. A number of variants of the SESAM model were investigated and compared to the previous assessment model, SXSA. These variants included the use (or not) of commercial cpue data, omission of the earliest years of data from the assessment, alternative settings for the detection threshold used to handle zero-valued data, and omitting the years of fishery closure when estimating the random walk variance on fishing mortality. The final SESAM model excludes commercial cpue data, omits 1983 data from the assessment and omits the years of fishery closure from the random walk variance calculation. Blim is set equal to Bloss based on quarter 4 SSB values to align with the new fishing season (1st November to 31st October). The short-term forecast is stochastic, which allows the probability of SSB being below Blim to be evaluated immediately following the fishing season.\n
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".