UKURAN MATA DAN SHORTENING YANG SESUAI UNTUK JARING INSANG YANG DIOPERASIKAN DI PERAIRAN TUAL ((Appropriate of Mesh Size and Shortening for Gillnet Operated on Tual Waters))
Bibliographic record
Abstract
ABSTRACTShortening and mesh size of gillnet that operated by Tual fishermen are various. The purposes of this study were to determine the effectively of gillnet based on different shortening and mesh size and to estimate the catch diversity index of each mesh size. The study was conducted from April 6th-May 15th of 2012 in Tual waters. Results shown that gillnet with mesh size of 2.25” and shortening of 50% caught the most number of fish (74). It was followed by gillnet of 2.50”-50% (59), 2.50”-55% (31), 2.25”-55% (24), 2.25”-45% (19), 2.50”-45% (15), 3.00”-50% (15) and 3.00”-55% (6). The Shannon index rate of gillnet with mesh size of 2.25” was 1.8, 2.50” (1.9) and 3.00” (1.1). While the Sympson index rate of gillnet with mesh size of 2.25” was 0.2, 2.50” (0.3) and 3.00” (0.4).Keywords: gillnet, mesh size, shortening, Tual waters-------ABSTRAKUkuran mata dan shortening jaring insang yang dioperasikan nelayan Tual sangat beragam. Penelitian ini ditujukan untuk menentukan efektivitas jaring insang yang didasarkan atas ukuran mata dan shortening yang berbeda dan menentukan indeks keragaman dari setiap ukuran mata. Penelitian dilakukan dari 6 April-15 Mei 2012 di perairan Tual. Hasil penelitian menunjukkan bahwa jaring insang dengan ukuran mata jaring 2,25” dan shortening 50% paling efektif menangkap ikan di perairan Tual dibandingkan dengan ukuran jaring lainnya. Jaring ini menangkap 74 ekor. Adapun jaring 2,5”-50% (59 ekor), 2,5”-55% (31 ekor), 2,25”-55% (24 ekor), 2,25”-45% (19 ekor), 2,50”-45% (15 ekor), 3,00”-50% (15 ekor) dan 3,00-55% (6 ekor). Indeks keragaman Shanon untuk jaring insang dengan ukuran mata 2,25” adalah 1,8, 2,50” (1,9) dan 3,00” (1,1). Sementara indeks keragaman Sympson pada ukuran mata 2,25” sebesar 0,2, 2,50” (0,3) dan 3,00” (0,4).Kata kunci: jaring insang, ukuran mata, shortening, perairan Tual
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".