MANAJEMEN USAHA PERIKANAN JARING INSANG DASAR DI KELURAHAN MANADO TUA 1 KOTA MANADO
Bibliographic record
Abstract
Abstract The study examines the bussiness management of bottom gillnet fishery in Manado Tua 1 village Manado city. This study aims to identify and assess fisheries management that includes venture capital, the catch, the marketing system, sharing system, labor system, performance of the functions of business management and financial analysis of the bottom gillnet fishery. The result of the study, the required capital of Rp. 4, 100,000. the catch is classified as demersal fish. Marketing system of fishermen, wholesaler, fish traider and consumers. But if it catches a bit of a marketing system directly to consumers. Sharing system 50% for owners and 50% for fishermen workers. The labour are needed 3-4 people. Keywords: Bussiness Management, bottom Gillnet, Manado Tua 1 Abstrak Penelitian ini mengkaji tentang manajemen usaha perikanan jaring insang dasar di KelurahanManado Tua 1.Penelitian bertujuan untuk mengetahui dan mengkaji manajemen usaha perikanan yang mencakup modal usaha, hasil tangkapan, sistem pemasaran, sistem bagi hasil dan sistem tenaga kerja, pelaksanaan fungsi-fungsi manajemen. Berdasarkan hasil penelitian, modal yang dibutuhkan sebesar Rp. 4.100.000.Hasil tangkapan ialah ikan yang tergolong demersal, sistem pemasaran dari nelayan, pedagang besar, pedagang pengecer, konsumen. Tetapi jika hasil tangkapan sedikit, sistem pemasaran yang dilakukan dari nelayan langsung kepada konsumen. Sistem bagi hasil 50% untuk nelayan pemilik dan 50% untuk nelayan pekerja. Tenaga kerja yang dibutuhkan 3-4 orang. Kata Kunci: Manajemen Usaha, jaring Insang Dasar, Manado Tua 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".