An Adapted Slipping Process to Exclude Jellyfish in the Sea of Marmara Purse Seine Fishery
Bibliographic record
Abstract
Excluding the jellyfish from the bunt-end is a common slipping process used in the Sea of Marmara purse seine fishery. For this aim, a sheet of netting piece, larger mesh size and thicker diameter, is rigged on the bunt-end of the purse seine net. The jellyfish mass on the netting piece are slipped by rolling over the headline (floating line) after partially hauling or drying-up the net while it is still in the water. In this study, the catch amount of this slipping was roughly estimated with the introduction of the slipping process only used by the purse seiners in the Sea of Marmara. There were eight successful purse seine operations conducted between 8 and 11 September 2018 in depth ranged 77 to 677 m. The percentage of landed species versus to jellyfish varied between 23% and 85%. The mean landed anchovy amount is 4379 (3756.6) kg for per operation. The mean slipped amount of jellyfish is 3812.5 (2404.4) kg. However, both anchovy (99.8%) and jellyfish (96.3%) are the vast majority species that landed and slipped, respectively. In the operations totally 100 boxes of anchovy (1180 kg) unintentionally was slipped with the jellyfish. In addition, two sharks with larger size were slipped to the sea as alive over the floating line of the net. Although slipping practised rarely in Turkey, all the purse seiner in the Sea of Marmara have to use the adapted slipped process to get rid of jellyfish. However, there are no records and scientific findings regarding slipped amount of the jellyfish. For this reason, this study is important to presented preliminary results regarding amount of the jellyfish. In conclusion, this study is extended completely the Sea of Marmara practised to understand the dimensions of jellyfish amount and slipping process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".