Efisiensi Usaha dan Faktor-Faktor Yang Mempengaruhi Produksi Pembenihan Ikan Patin
Bibliographic record
Abstract
Catfish is a freshwater fish in Indonesia which is distributed mostly in Sumatra and Kalimantan. The research was conducted in Metro City with 30 samples used. Sampling technique with census method. The analysis tools are multiple linear regression analysis with the Cobb-Douglas production function, technical efficiency analysis with the Data Envelopment Analisys (DEA) approach, price/allocation efficiency analysis and economic efficiency analysis. The results showed that 1) capital production factors (X2), number of brood stock (X3), and drugs (X9) had a significant effect on the production of catfish hatcheries. 2) technical efficiency value of 0.940. 3) price/allocation efficiency value of 194,098. 4) economic efficiency value of 182,452.
 
 Keywords: Economic efficiency, price/allocation efficiency, technical efficiency, catfish
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".