Baltic International Fish Survey Working Group (WGBIFS)
Bibliographic record
Abstract
The Baltic International Fish Survey Working Group (WGBIFS) plans, coordinates, and imple-ments demersal trawl surveys and hydroacoustic surveys in the Baltic Sea including the Baltic International Acoustic Survey (BIAS), the Baltic Acoustic Spring Survey (BASS), and the Baltic International Trawl Surveys (BITS) in the 1st and 4th quarter on an annual basis. The group com-piles results from these surveys and provides the herring, sprat, cod and flatfish abundance in-dices for the Baltic Fisheries Assessment Working Group (WGBFAS) to use as tuning fleets. \nIn 2023, WGBIFS completed the following tasks: (1) compiled survey results from 2022 and the first half of 2023, (2) planned and coordinated all Baltic fish stocks assessment relevant surveys for the second half of 2023 and the first half of 2024, (3) updated the common survey manuals according to decisions made during the annual WGBIFS meeting. Data from the recent BITS was added to the ICES Database of Trawl Surveys (DATRAS). The Tow-Database was corrected and updated. The Access-databases for aggregated acoustic data and the ICES database of acoustic-trawl surveys for disaggregated data were updated. All countries registered collected litter ma-terials to DATRAS. \nThe area coverage and the number of control hauls in the BASS, BIAS and GRAHS in 2022 were considered to be appropriate to the calculation of tuning indices and the data can be used for the assessment of Baltic herring and sprat stocks. The number of valid hauls accomplished during the 4th quarter 2022 and 1st quarter 2023 BITS were considered by the group as appropriate to tuning series and the data can be used for the assessment of Baltic and Kattegat cod and flatfish stocks. BIAS and BASS survey sampling variance calculation questions were discussed and standard deviation for Central Baltic herring acoustic index series calculated. \nIn comparison exercises between the StoX survey computational method and traditional IBAS calculation methods it was found that the StoX project, developed for the WGBIFS, has small methodological differences compared to the standard calculation method used by the group, as specified in the Manual for the International Baltic Acoustic Surveys (IBAS), and is thereby caus-ing a small difference in the total number of herring and sprat., The work with transition to a more transparent calculation software (e.g. StoX) will continue during the next period with more thorough analysis of calculation methodologies. \nA further comparison exercise between the StoX method and traditional Gulf of Riga Herring Survey calculation method was performed using data from 11 last years. It showed no major differences in herring total abundance estimates for most of the years. However, notable differ-ences were in the age compositions of those two methods. Some errors and differences in input data (uploaded into the ICES database) were found and therefore the further analysis was post-poned until these issues are fixed. \nWGBIFS is planning to continue with analogical comparison exercises in the coming years before the final transition to a transparent reproducible pathway into the ICES Transparent Assessment Framework (TAF) can be done. Work towards transitioning to TAF will continue during the next 3-year period until all methodological and database differences are resolved. \nInquiries from other ICES expert groups were discussed and addressed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".