MétaCan
Menu
Back to cohort
Record W2283229213

World production of pig meat and its tendencies

2004· article· en· W2283229213 on OpenAlexaboutno aff
Dragan Miladinović, Tadija Stamenković

Bibliographic record

VenueTehnologija mesa · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Trade and Competitiveness
Canadian institutionsnot available
Fundersnot available
KeywordsPig ironChinaGeographyAgricultural economicsEnvironmental protectionArchaeology
DOInot available

Abstract

fetched live from OpenAlex

This paper analyses the state of production of pig meat in the world, by continents and by countries. It is established that the total production of pig meat is increasing. It amounted to 71,155,000 tons in 1991, and 90,909,000 tons in 2000. Tendency of annual increase in pig meat production in the period 1991-2000 was positive and amounted to 1,815,497 tons. Observing by continents, the highest pig meat production in 2000 was recorded in Asia (50,149,000 tons), then in Europe (22,625,000 tons) and North and Middle America (11,560,000 tons).Tendency of pig meat production in 1991-2000 in all continents was positive, except for Africa, where it was negative. The leading pig meat producers in 2000. were: China (43,058,000 tons), USA (8,532,000 tons), Germany (3,850,000 tons), Spain (2,962,000 tons), France (2,315,000 tons), Poland (1,900,000 tons), Brazil (1,804,000 tons), Canada (1,675,000 tons), Denmark (1,650,000 tons), The Netherlands (1,643,000 tons), Italy (1,475,000 tons), Japan (1,250,000 tons), Belgium Luxembourg (987,000 tons) and G. Britain (923,000 tons).. Positive tendencies in pig meat production in 1991 - 2000 were recorded in the following countries: China, USA, Spain, Brazil, Canada, Denmark, France Germany, Italy, Belgium - Luxembourg, G. Britain, Yugoslavia, Poland, The Netherlands, India, Argentina, New Zealand, Slovenia and Croatia. Negative tendencies in pig meat production in 1991 - 2000. were recorded in: Austria Hungary, Romania, Russian Federation, Czech Republic, Japan, Venezuela and Bosnia and Herzegovina.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.771
Threshold uncertainty score0.337

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 head0.028
GPT teacher head0.216
Teacher spread0.188 · 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 designTheoretical or conceptual
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

Citations0
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueTehnologija mesaSame topicGlobal Trade and CompetitivenessFrench-language works237,207