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Record W2792702270 · doi:10.5897/jdae2017.0870

Economic analysis of duck eggs incubation using hot spring as heat source

2018· article· en· W2792702270 on OpenAlexfundno aff
J. Taplah Jr. Anthony, Carl Delfin, Marie C. Amongo Rossana, Óscar Fernando, P. Tokpah David

Bibliographic record

VenueJournal of Development and Agricultural Economics · 2018
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsnot available
FundersAlzheimer Society Research ProgramUniversity of the Philippines
KeywordsIncubatorProfitability indexPayback periodAgricultural scienceProduction (economics)Operations managementEnvironmental scienceElectricityBusinessAnimal scienceEngineeringBiologyEconomicsElectrical engineeringFinance

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.221
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2018
Admission routes1
Has abstractyes

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