Total meat production and its tendencies
Bibliographic record
Abstract
This paper analyses the state of total world meat production, by continents and by countries. It is established that the total world meat production is constantly increasing. It amounted to 236.541.000 in 2001 and 265.106.000 tonnes in 2005. Tendency of annual increase from 2001 to 2005 was 5.454.000 tonnes. Since 2005 the highest total meat production (by continent) was in Asia (111.835.000 tonnes), followed by Europe (52.912.000 tonnes), North and Central America (51.321.000 tonnes), South America (31.088.000 tonnes), Africa (12.110.000 tonnes) and Oceania (5.841.000 tonnes). Tendency of total meat production from 2001 to 2005 on all continents was positive. It is the highest in Asia, followed by Europe, South America, North and Central America, Africa and Oceania. The leading meat producers in 2005 were: China (77.564.000 tonnes), The USA (39.556.000 tonnes), Brazil (19.919.000 tonnes), Germany (6.884.000 tonnes), India (6.297.000 tonnes), France (6.179.000 tonnes), Spain (5.736.000 tonnes), Mexico (5.040.000 tonnes),The Russian Federation (4.885.000 tonnes), Canada (4.680.000 tonnes), Argentina (4.175.000 tonnes), Italy (4.099.000 tonnes) and Australia (3.946.000 tonnes). Positive tendencies in total meat production (2001-2005) were found in: Austria, Belgium-Luxemburg, Bosnia and Herzegovina, Great Britain, Denmark, Germany, Poland, The Russian Federation, Slovenia, Croatia, Spain, Japan, China, India, South Africa, Canada, The USA, Mexico, Brazil and Australia. Negative tendencies in total meat production (2001-2005) were recorded in: Italy, Macedonia, Romania, Hungary, Ukraine, France, The Netherlands, Czech Republic and Argentina. .
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".