Report of the Scallop Assessment Working Group (WGScallop) ,10-12 October 2018 York, UK
Bibliographic record
Abstract
The ICES Scallop Assessment Working Group (WGScallop) met in Aberdeen (2016), Belfast (2017) and York (2018) with an average of 16 participants from 8 countries. The main terms of reference for the working group were to update and provide data and exchange knowledge on the various scallop fisheries in the Northeast Atlantic region. This included the compilation of available scallop fisheries data and the production of maps to better identify stock boundaries and inform spatial management. The WG also considered the various assessments for scallop stocks and reviewed the recent developments in the English Channel. The scallop stocks in the Baie ds Seine have experienced extremely large recruitment events over the past few years and the group has discussed the fisheries management measures in this region. Over the last few years, the English Channel has also seen the re-introduction of a scallop data collection programme, a cooperative industry survey and stock assessment. The WG reviewed the approaches and made recommendations on the methodologies which were accepted and included in the most recent assessment. A main focus of the group has been understanding the scallop ecosystem and the impacts of fishing. A number of work areas have been delivered which examined marine spatial planning and potential benefits of seasonal closures, Marine Protected Area’s and European marine sites as conservation zones and possible recruitment supply areas for scallop populations. A number of projects have recently been established through the EU Interreg programme and the WG is keen to see how these develop over the next few years. The WG has made significant progress in terms of establishing an international scientific forum where resources, knowledge, experience and insights can be exchanged. This is evident in the recent advances in the use of use of camera systems to complement existing survey work. Image surveys are expanding due to information exchange; presently they are being conducted in Canada, Iceland and the United States; further cameras are increasingly being used to examine dredge performance and habitat and are used or being trialled in various UK surveys. The overall objective of the group continues to be providing scientific advice on scallops and defining a common approach to the assessment of scallop stocks. In 2018, for the first time all stocks were assessed using an independent fisheries survey. The WG understands there are still limitations and uncertainties surrounding future funding sources for surveys and are also considering fishery dependent indicators and their possible uses to inform stock status. The growing need for global assessment and advice of scallops is becoming increasing apparent and the group plans to use the Baie Des Seines/English Channel and the Irish Sea/Isle of Man fisheries as case studies to explore possible management frameworks and to continue with the progress of assessments for all scallop stocks.\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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.162 | 0.058 |
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".