ICES-FAO Working Group on Fishing Technology and Fish Behaviour (WGFTFB)
Bibliographic record
Abstract
The Working Group on Fishing Technology and Fish Behaviour (WGFTFB) discusses and re-views research and practices of fishing technology and fish behaviour in relation to commercial and survey gears. The group provides guidance for management including, inter alia, the impacts of fishing on the environment. The working group is jointly supported by the International Council for the Exploration of the Sea (ICES) and the Food and Agriculture Organization of the United Nations (FAO), which have fostered a fruitful working relationship in an international forum. WGFTFB also collaborates with the Working Group on Fisheries, Acoustics, Science and Technology (WGFAST) to facilitate the interdisciplinary exchange of knowledge and to foster cooperation.Due to the COVID-19 pandemic and the suspension of the Russian Federation from ICES due to the war in Europe, the group did not meet physically in during the period 2020 to 2022.The 2023 annual meeting was hosted by the Department of Fisheries, Government of India, or-ganized by BOBP-IGO and NFDB in collaboration with ICAR Fisheries Institutes and State Fish-eries Universities, and run in parallel to the symposium on “Innovations in Fishing Technologies for Sustainable and Resilient Fisheries. The meeting included several plenum sessions, a series of Topic Group meetings and sessions dedicated to Working Group matters including presenta-tion of National Reports; selection of new chairs; and work plan development. The three Topic Groups examined issues relating to passive gear, performance indicators for fishing gears and abandoned, lost or otherwise discarded fishing gear (ALDFG).In addition to the work conducted during the meeting, this report contains the national reports describing activities in different countries. National reports are structured to give an overview of current and planned activities in the institutes and organizations of the county that are active in research in fishing gear and fish behaviour and are an important tool to stimulate collaborative research. Current national reports cover a broad field of gear technology research, including re-search related to bycatch reduction of target and bycatch species, minimizing the effect on the marine environment, pollution, and energy efficiency. In addition to classical gear technology approaches, several projects are presented that focus on techniques, such as computer vision.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.014 |
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".