Determination of gill-net selectivity for King Fish (Scomberomorus Commerson, Lacepede 1800) using Mesh size in Sungailiat, Bangka Belitung Province
Bibliographic record
Abstract
Abstract Selectivity is a fishing gear characteristic capable of catching fish of a specific size and species from the existing population distribution. Mesh size is one of the important factors that influence gillnets’ selectivity, because different length-classes of fish are caught by them. This report focuses on the selectivity of two mesh sizes of gillnets (8.89 cm and 10.16 cm) on mackerel's catch and constructing a gillnet in Sungailiat waters. Experimental fishing methods and selectivity analysis were used for the fishing gear’s more selective preparation, if the largest fishing ratio's value at the fork length (L) is high and the standard deviation value (S) is closer to 1. It is concluded that to be more selective a largest S (L) value of 0.999267194 at fork length (L=54.95 cm), and with a larger value of S (L) 0.997096071 and with (L=66.55 cm) for mesh Sizes 8.89 cm and 10.16 cm, respectively.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".