Analisis Pola Pertumbuhan, Faktor Kondisi dan Eksploitasi Madidihang (Thunnus albacares) yang Didaratkan di TPI Linau Kabupaten Kaur
Bibliographic record
Abstract
Ikan Tuna Sirip Kuning merupakan jenis ikan yang penting secara ekonomi, menyebar hampir ke seluruh perairan Indonesia termasuk Perairan Kaur. Salah satu parameter terkait upaya pemanfaatan berkelanjutan adalah pola pertumbuhan dan eksploitasi, faktor ini merupakan salah satu aspek dalam memperkirakan kondisi populasi ikan Tuna Sirip Kuning di perairan yang meliputi perkiraan usia, pertumbuhan, dan kematian. Tujuan penelitian untuk mengidentifikasi aspek biologis termasuk distribusi ukuran, parameter pertumbuhan dan laju eksploitasi ikan Tuna Sirip Kuning yang di daratkan di Kabupaten Kaur. Penelitian dilakukan pada September 2022 - Agustus 2023. Pengambilan sampel dilakukan di TPI Linau, Kabupaten Kaur. Data meliputi panjang dan berat ikan. Ikan Tuna Sirip Kuning diperoleh 275 sampel ikan tuna sirip kuning dengan interval kelas 68-193 cmFL. Hubungan antara panjang dan berat badan termasuk dalam alometrik negatif 2,91 dengan nilai faktor kondisi (Kn) 123-1,45. Parameter pertumbuhan menunjukkan nilai panjang asimtotik (L∞) 194,25 cmFL, dengan nilai koefisien pertumbuhan (K) 0,33 tahun-1 dan nilai Lc 146 cm. Perkiraan tingkat kematian dan eksploitasi adalah total kematian (Z) 1,11 tahun-1, mortalitas alami (M) 0,48 tahun-1, mortalitas akibat penangkapan (F) 0,63 tahun-1, tingkat eksploitasi (E) 0,57 tahun-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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".