Theme session M - Mixed fisheries within a changing ecosystem and socio-economic landscape
Bibliographic record
Abstract
ICES Annual Science Conference Book of abstracts of theme session M: Mixed fisheries within a changing ecosystem and socio-economic landscape Conveners: Claire Moore (Ireland), Dorleta Garcia (Spain), Paul Dolder (UK) CM 37: Status and management of mixed fisheries: a global synthesis CM 42: An effort to identify métiers in longline fisheries in western Greek waters to more efficiently address management challenges related to their catches, landings and discards CM 121: Minimizing unwanted bycatch through gear selectivity: A MSE study on NEA haddock CM 144: An efficient MSE framework for exploring multispecies interactions and management strategies of pelagic stocks in the Norwegian Sea CM 147: Exploiting mixed fishery constraints to reconstruct past fishing mortality – a century of fishing in the North Sea CM 153: Spatial closure or effort reduction as a tool for managing mixed fisheries in the Gulf of Lion, Western Mediterranean Sea CM 158: Spatial patterns in retained catches in mixed-fisheries CM 178: Tackling technical interactions in Ecosystem-Based Fisheries Management through Management Strategy Evaluation CM 209: Mixed performance: using historic catch data to assess the accuracy of Mixed Fisheries models in predicting catches of non-target species CM 215 Revealing harvesting patterns from fishing trips CM 233: Exploration of measures to protect bycatch species in the demersal mixed fisheries of the North Sea CM 315: Identifying targeting or avoidance behavior for catch rate index development in a mixed species fishery CM 331: A bioeconomic mixed-fisheries model for Irish fisheries CM 337: Technical measures for a better scientific knowledge: gear selectivity in the north Spanish bottom trawl fishery. CM 375: Are the drivers of discarding by the demersal trawl fishery in the Celtic Seas ecoregion predominantly regulatory in nature? CM 395: Investigating the concept of target species in mixed fisheries through the spatio-seasonal analysis of target patterns CM 397: Evaluation of the demersal mixed fisheries multiannual management plan in the Bay of Biscay including non-target and bycatch stocks CM 405: Framework to operationalise MSE for mixed fisheries CM 490: Adding biological realism to the mix – Lessons learned from the North Sea mixed fisheries FLBEIA model CM 491: An Indicator Framework Approach to Support Sustainable Fisheries Management in the Mediterranean CM 496: Evidence for avoidance of potential choke species in a mixed stock groundfish fishery with 100% at-sea monitoring CM 515: Evaluating the conservation and economic risks of MEY and MSY based harvest strategies for a mixed-species flatfish fishery in British Columbia, Canada
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.232 | 0.061 |
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".