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Record W2606567328 · doi:10.22004/ag.econ.245194

MILK PRODUCTION IN SERBIA AND POSITION SMALL FARMERS

2010· article· en· W2606567328 on OpenAlexaboutno aff
Božo Drašković, Zoran Rajković, Dusan Kostic

Bibliographic record

VenueAgEcon Search (University of Minnesota, USA) · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Development and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultural scienceProduction (economics)Milk productionLivestockBusinessPosition (finance)Agricultural economicsGeographyAnimal scienceEconomicsBiology

Abstract

fetched live from OpenAlex

The small farmers participate significantly in the total cow milk production in Serbia. Milk production stabilized at around 1.6 billion liters per year and one half of the total quality is purchased and processed in dairies, and the other half is spent and/or processed in rural farms in cheese and cream and sold in markets. The production(arranged by the farmers themselves), and marketing of finished products on markets, by rule, is organized by small farmers who have less than 10 cows. The farmers who have more than 10 milked cows have no technical conditions to reproduce milk themselves and they are forced to give the total amount of milk to dairies. The dominant position of small farmers in milk production is a result of declining farms. On small farms it is not possible to organize a massive and profitable production. A milked cow gives about 2.6 thousand liters, per year in Serbia and in the US, Canada and some EU countries more than 6 thousand liters of raw milk. The reduced number of farms and a small number of cattles have resulted in insufficient use, otherwise good natural resources for livestock development in Serbia.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.196
Teacher spread0.160 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2010
Admission routes1
Has abstractyes

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