FAKTOR-FAKTOR YANG MEMPENGARUHI PENDAPATAN NELAYAN TANGKAP DI DESA TABANIO KECAMATAN TAKISUNG KABUPATEN TANAH LAUT
Bibliographic record
Abstract
This research was conducted to know: the cost factor of care, operational cost, depreciation cost and the means of catching together to income and know the most dominant factor influence the catching fisherman income in Tabanio Village, Takisung Sub-District, Tanah Laut Regency.This thesis research uses quantitative method with numbers such as maintenance cost, operational cost, fishing gear and depreciation cost. Meanwhile, the data used to analyze the thesis is using cross section data. With primary material that is taken a direct data from source, that is obtained from direct interviews from fishing boat owner in Tabanio Village, Takisung District, Tanah Laut Regency.After doing research, it is known that simultaneously the cost factor of care, operational cost, depreciation cost and fishing gear have an effect on to catch fisherman income and appliance catch variable (X3) is the factor which has the most dominant influence to catch fisherman's income in Tabanio Village, Takisung District Tanah Laut District.Keywords:: Maintenance Cost, Operational Cost, Depreciation Cost, Fisherman's Income Catch
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.028 | 0.003 |
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".