ANALISIS FASE BULAN TERHADAP HASIL TANGKAPAN PURSE SEINE DI PERAIRAN AMAHAI, PULAU SERAM
Bibliographic record
Abstract
Intensitas cahaya yang diterima perairan berubah sesuai dengan fase bulan, yang berdampak pada perilaku ikan yang memiliki sifat fototaksis positif atau negatif terhadap cahaya. Hal ini secara langsung mempengaruhi volume hasil tangkapan nelayan. Pemahaman yang kurang mendalam mengenai pengaruh fase bulan ini membuat nelayan seringkali tidak dapat memaksimalkan hasil tangkapan mereka. Tujuan penelitian ini adalah menganalisis pengaruh fase bulan terhadap hasil tangkapan purse seine dan komposisi hasil tangkapan purse seine berdasarkan fase bulan di Perairan Amahai. Penelitian ini dilakukan pada bulan Februari-Maret 2024 yang bertempat di Perairan Amahai, Pulau Seram. Metode yang digunakan dalam penelitian ini yaitu metode survei dengan melakukan observasi secara langsung di lapangan. Hasil analisis menunjukan Fase bulan tidak berpengaruh signifikan terhadap hasil tangkapan purse seine dimana nilai Signifikasi sebesar 0.529 > 0,05. Hasil tangkapan purse seine sebanyak 25.570 Kg dengan komposisi hasil tangkapan pada fase bulan New Moon yakni ikan momar (Decapterus sp) 62 %, ikan selar (Selar sp) 38%. Komposisi hasil tangkapan pada fase bulan First Quarter yakni ikan layang (Decapterus sp) 63 %, ikan selar (Selar sp) 37%. Komposisi hasil tangkapan pada fase bulan Full Moon yakni ikan layang (Decapterus sp) 57%, ikan selar (Selar sp) 30 %, ikan cakalang (Katsuwonus pelamis) 13%. Sedangkan pada fase bulan Last Quarter komposisi hasil tangkapan yakni ikan layang (Decapterus sp) 66%, ikan selar (Selar sp) 33%, dan ikan cakalang (Katsuwonus pelamins) 1%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".