Удосконалення умов утримання кнурів закордонної селекції, вплив моціону на виробництво сперми
Bibliographic record
Abstract
У статті наведено матеріали про використання моціону та удосконалення технологічних умов утримання кнурів зарубіжної селекції. Також вивчено вплив моціону на виробництво та якість сперми в умовах діючого господарства ДП «Націонал Плюс» ПП «Націонал» Дніпропетровської області. Встановили, що впровадження моціону та удосконалення умов утримання кнурів закордонної селекції позитивно вплинуло на фізіологічний стан кнурів, що призвело до покращання якості сперми. В умовах господарства виявлено кращі зарубіжні генотипи кнурів-плідників, основними з яких є термінальні кнури лінії Macster (канадської селекції), які за всіма показниками переважали всі інші. In the article materials are given on the use of exercise and the improvement of technological conditions for keeping of boars of foreign selection. Also the impact of the exercise on the production and quality of semen under the conditions of the current economy of the state enterprise «National Plus» of the private enterprise «National» of the Dnipropetrovsk region was studied. We established that the introduction of the exercise and the improvement of the conditions of keeping of boars of foreign breeding positively influenced on the physiological state of the boars, which led to an improvement in the quality of the sperm. Also in the conditions of farming, the best genotypes of boars-producers are revealed. One of the main is the terminal boars of the Macster line (Canadian breeding), which in all respects exceeded all other boars.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.014 |
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".