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Record W1869133077 · doi:10.22437/jiiip.v0i0.167

Faktor-faktor Yang Mempengaruhi Pendapatan Usaha Penggemukan Sapi (Kasus di Kelurahan Ekajaya, Kecamatan Jambi Selatan Kotamadya Jambi)

2009· article· en· W1869133077 on OpenAlex
Sambas Mulyana

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJurnal Ilmiah Ilmu-Ilmu Peternakan · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLivestock Farming and Management
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsDepreciation (economics)Research ObjectRevenueLabor costAgricultural scienceCageBusinessEconomicsMathematicsEngineeringBiologyFinanceBusiness administrationEconomic growthHuman capital

Abstract

fetched live from OpenAlex

A research was conducted to determine the influence of cost of sapi bakalan, cost of labour, cost of cage depreciation, and cost of cage equipment to income of farmer, in Kelurahan Ekajaya, Kecamatan Jambi Selatan, Jambi Regency. Research was done through survey. Thirty household of farmer keep cow as fattening object were unit of the research drawn by simple random sampling. Data was analyzed by descriptive method and Multiple regression. Result of this study showed that cost of sapi bakalan Rp.3.360.000 (57,36%), cost of labour Rp.2.000.000, cost of cage depreciation Rp.450.000 (7,68%), and cost of cage equipment Rp.48.000 (0,82%), totally Rp.5.858.000. Revenue comes from value of cow was sold Rp.10.530.000, so the farmer income was Rp.4.642.000. Simultaneously cost of sapi bakalan, cost of labour, cost of cage depreciation, and cost of cage equipment , influences the farmer income, and partially cost of sapi bakalan and cost of labour significantly influence farmer income.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.013
GPT teacher head0.215
Teacher spread0.202 · 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