Тechnological solutions at a dairy complex in the wilderness
Bibliographic record
Abstract
Recently, the creation of large livestock complexes has been observed on all continents of the earth. High-yielding cattle herds of dairy cows exist in Holland and Israel, meat breeds - in Argentina and Brazil, pigs - in Denmark and the USA, sheep - in Australia and New Zealand, poultry - everywhere. In Saudi Arabia, the government has taken a decisive course on progressive policies in agriculture. The Al-Safi company produces 75,000 tons of milk per year (7000 kg per cow). It has its own processing plant (300 tons per day). The company employs more than 1,100 people in 137 different positions on a competitive basis. The farm has more than 25000 head of cattle, 225 people work, which is 89 heads per person and 125 cows per milker. Many cows give 10000 kg of milk per lactation (more than 30 kg per day). There are 17 milking parlors and 370 milking machines. The technology of keeping animals includes 5 subdivisions - newborn calves, workshops for raising young animals (heifers, steers for fattening), and an inseminated non-calf workshop. The load per worker is 260 heads. People work from 5 a.m. to 10 a.m. and from 3 a.m. to 6 p.m., they are provided with showers, wall lockers, a washing machine, overalls and shoes, and personal hygiene products. To improve the gene pool of a closed population, artificial insemination is used. Every day, more than 30 calves are born at the farm, which are placed in cages (100 Ч 200 Ч 80 cm) of a calf house without walls. At the age of three months, they are transferred to a group up to a year, then heifers - to a group for fertilization, steers - for fattening. Netels are formed into a separate group. The program of uniform feeding of livestock is based on the use of silage and hay forage with a two-year supply, which ensured the stability and quality of forage, as well as crop rotation. For this purpose, there are silo facilities for 35,000 tons. Hay is harvested in bales or rolls. Organization of breeding and selection of animals is carried out according to a computer program. There are also similar high-tech enterprises in Israel, the USA, Holland and Canada. The talented people of Ukraine are not far behind the Arabs in terms of intelligence, the country's leaders are no stupider than princes and sheikhs, Ukrainian businessmen are no poorer than Arabs, although the sources of income are different. It is necessary to improve the management structure and the quality of the state apparatus, to increase punishment for inaction and fraud. The production and consumption of food determines the well-being of the people, which is so eagerly talked about "above", but there are few effective measures. Everyone must fight for the best together, using both domestic and foreign experience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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 teacher head, 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".