Economic analysis of duck eggs incubation using hot spring as heat source
Bibliographic record
Abstract
The economic analysis of a manual and automatic turning hot spring incubator developed at the University of the Philippines-Los Baňos, Laguna Province, Philippines, was studied by comparing its performance with the conventional electrical incubator. This research can help small-scale farmers by reducing operational cost and increasing profitability. To determine the feasibility, undiscounted and discounted measures were used. For undiscounted measures, the break-even point (BEP) and payback period (PBP) for the conventional electrical incubator, manual and automatic turning hot spring incubator were 2788, 1950 and 4552 balut; and 2.31, 0.751 and 1.755 years for balut production; 723, 660 and 1540 ducklings; and 1.852, 0.856 and 1.998 years for duckling production, respectively. For the discounted measures, benefit-cost ratio (BCR), net present value and internal rate of return (IRR) for the conventional electricity incubator were 1.07, 1.36 and 1.20; ₱4176.88, ₱34359.81 and ₱22357.39; and 7.16, 36.44 and 20.35, respectively, for balut production and 1.15, 1.44 and 1.16; ₱6264.42, ₱28320.29 and ₱12740.51; and 15.15, 43.78 and 15.88, for ducklings production, respectively. Finally, the outcome of this research can be adopted by balut producers that have access to hot spring. The hot spring manual turning incubator was the most feasible incubator for balut production than the conventional incubator. Key words: Hot spring incubator, conventional, manual, automatic, ducklings, economic analysis, balut.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".