Analysis Production and Technical Efficiency of Tilapia (Oreochromis niloticus) In Freshwater, Tasikmalaya
Bibliographic record
Abstract
The fisheries subsector plays a significant role in the national GDP. In 2023, its contribution reached 2.66%, increasing from 2.58% in 2022. In the second quarter of 2024, the subsector contributed 2.54%, higher than the 2.34% recorded in the first quarter. One of its leading commodities is tilapia (Oreochromis niloticus), a freshwater species valued for its high economic potential and strong adaptability. In 2021, West Java reported the highest national tilapia production at 270,925 tons, with Tasikmalaya City contributing the largest share at 2,189 tons. This study analyzes the technical efficiency of tilapia farming in Tasikmalaya using the Cobb–Douglas Stochastic Production Function. The research was conducted from July - September 2024 in the districts of Bungursari, Kawalu, Purbaratu, and Cibeureum. Locations were selected purposive, and a snowball sampling technique yielded 52 respondents. The results indicate that seed and pellet feed exert a statistically significant effect on production, whereas bran, EM4, cultivation duration, and labor inputs do not show a significant influence. The technical inefficiency model demonstrates that farmer experience and education have a significant impact on efficiency. Experience contributes to technical inefficiency, while education mitigates it.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 0.000 |
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".