ANALISIS HASIL TANGKAP IKAN TONGKOL (Euthynnus Affinis) TERHADAP PEREKONOMIAN NELAYAN DI UPT. PELABUHAN PERIKANAN PANTAI PASONGSONGAN KABUPATEN SUMENEP
Bibliographic record
Abstract
Telah dilakukan penelitian mengenai analisis hasil tangkap nelayan apakah dapat mempengaruhi perekonomian nelayan dan analisis hasil tangkap ikan tongkol memepengaruhi terhadap perekonomian nelayan di UPT. Pelabuhan Perikanan Pantai Pasongsongan Dusun Lebak Sari Kecamatan Pasongsongan Kabupaten Sumenep Madura Jawa Timur. Penelitian ini bertujuan untuk menganalisis hasil tangkap nelayan dan pendapatan ikan tongkol apakah dapat mempengaruhi perekonomian nelayan di UPT. Pelabuhan Perikanan Pantai Pasongsongan Kabupaten Sumenep. Metode yang digunakan dalam penelitian ini yaitu menggunakan metode analisis regresi logistik dengan menggunakan uji t dan hipotesis. Penelitian menunjukkan bahwa analisis hasil tangkap nelayan dapat mempengaruhi terhadap perekonomian nelayan hasil penelitian dengan pengujian hipotesis dan uji t menunjukkan bahwa untuk pengaruh pendapatan (X1) terhadap perekonomian (Y) adalah sebesar 0,000 < 0,05 dan nilai t hitung 4,695 > t table 2.020, sehingga dapat disimpulkan bahwa H1 diterima yang berarti terdapat pengaruh X1 terhadap Y. Untuk analisis hasil tangkap ikan tongkol terhadap perekonomian nelayan hasil dengan penelitian pengujian hipotesis dan uji t menunjukkan bahwa untuk pengaruh pendapatan (X2) terhadap perekonomian (Y) adalah sebesar 0,003 < 0,05 dan nilai t hitung 19,484 > t table 4.303, sehingga dapat disimpulkan bahwa H1 diterima yang berarti terdapat pengaruh X2 terhadap Y.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".