PENGELOLAAN PERIKANAN HIU DI PELABUHAN PERIKANAN PANTAI TEGALSARI TEGAL
Bibliographic record
Abstract
Ikan hiu adalah jenis ikan bertulang rawan (Elasmobranchii) yang berperan sebagai predator puncak dalam rantai makanan dan juga memiliki peranan penting dalam menjaga keseimbangan ekosistem di lautan. Saat ini, keberadaan hiu terancam punah akibat aktivitas penangkapan berlebih yang disebabkan meningkatnya permintaan komoditas sirip di pasar internasional. Salah satu basis pendaratan hasil tangkapan hiu di Indonesia terletak di Pelabuhan Perikanan Pantai (PPP) Tegalsari Kota Tegal, Provinsi Jawa Tengah. Tekanan terhadap populasi hiu di kawasan ini tidak hanya berasal dari peningkatan usaha tangkapan, namun juga dari kondisi perairan dan habitat yang terus mengalami degradasi. Penelitian ini bertujuan untuk menganalisis kinerja pengelolaan perikanan hiu di PPP Tegalsari dengan pendekatan Ecosystem Approach to Fisheries Management (EAFM) serta menyusun rekomendasi untuk tindakan pengelolaan. Hasil penelitian menunjukkan bahwa kinerja pengelolaan perikanan hiu di PPP Tegalsari berada pada kondisi baik dengan nilai rata-rata keseluruhan domain sebesar 73,39. Tindakan pengelolaan diprioritaskan pada domain ekonomi dan sumber daya ikan yaitu diversifikasi usaha dan kemudahan penyediaan akses permodalan bagi rumah tangga perikanan, membuat regulasi pembatasan upaya penangkapan dan ukuran minimal ikan hiu yang boleh ditangkap; serta peningkatan pengawasan terkait selektivitas alat tangkap dan metode penangkapan.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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".