Breeding, artificial insemination and recording of cattle in Iran
Bibliographic record
Abstract
Breeding of cattle has been traditionally practised in Iran for centuries. According to different authors cattle were domesticated for the first time in Asia Minor and Iran and later transported to India and Europe. The most famous Iranian cattle breeds are Sarabi, Golpaygni, Mazandarani, and Sistani. There are about 7.5 million cattle and 500 thousand water buffalo in Iran. Yazdizadeh accomplished the first crossbreeding in Iran in the 1940, following the import of purebred cattle from France. Hamedi performed the first artificial insemination in Iran in 1936. After that the Ministry of Agriculture established an insemination centre in Tehran in 1950. Currently there are 7 artificial insemination centres with 143 proved bulls in Iran, which produce 1.03 million semen doses per year. The importation of cattle was continued at a commercial level during 1950s and 1960s initially from the European countries and then from the USA and Canada as well. The major breeds at that stage were Brown Swiss, Jersey, Red Danish, Simmental and Holstein, which were initially kept in large farms near Tehran. However there are currently about 750 thousand purebred cattle, mostly Holstein breed (> 95%), kept near big cities throughout Iran. The Institute for animal breeding and milk improvement has started the recording of purebred cattle in 1983. About 36% of purebred cattle in Iran, which are kept in 1883 private farms, are monitored by this institute. The number of registered bulls is 6802. This institute reported a range of genetic gain in milk production from 62.49 to 315.55 kg per lactation, accompanied by a decrease in fat percentage of -0.02 to -0.06 over 10 years. The average milk production of recorded cattle (68312 heads) was 6519 kg/305 days. Seven native breeds have also been recorded in 8 breeding centres from the 1950s. There are currently 8000 recorded native cattle in Iran.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".