PREDICTION OF COMFORT FOR DAIRY COWS, DEPENDING ON THE STATE OF THE ENVIRONMENT AND THE TYPE OF BARN
Bibliographic record
Abstract
Material and resource conservation are important when choosing the optimal technology for keeping dairy cattle.The so-called "Canadian technologies" of frame construction that are widely used in the world are only relatively recently used in national animal husbandry.The question of ensuring the comfort of animals in such rooms remains controversial, since the climate in them is as close as possible to environmental conditions.The purpose of the study was to study the temperature and humidity regime of uninsulated rooms and assess the state of comfort of animals in barns of the frame and hangar type.The temperature and humidity of the air were measured inside and outside the premises (n = 827) periodically from January to July 2018 (in the temperature range from -7.8 to +34.2°С).Using the multiple linear regression function in STATISTICA 10 (StatSoft, Inc., 2011), the calculated temperature values in the barns for low and high temperatures of the Steppe of Ukraine were obtained.It has been established that the temperature-humidity regime of uninsulated rooms is as close as possible to the state of the external environment and depends on the design features (type) of the barn. Chapter «Veterinary communications»The temperature difference inside and outside the premises will be 3-5°C.It is necessary to provide additional space cooling (axial fans of large diameter, small-drop irrigation, as well as their combination), since the temperature-humidity index (ТHI) inside the barn will be 2-3 units higher.The material of the article will be useful for breeders when choosing a technology for keeping dairy cows.Calculated values of temperatures and THI in uninsulated rooms of frame and hangar type can be used as approximate data for assessing the comfort of cows in lightweight rooms in conditions of temperate continental climate of the Steppe of Ukraine.The values of temperature and relative humidity of air in the barns obtained by us, as well as the temperature-humidity index (as an indicator of the comfort of animals in hot conditions), require practical confirmation under conditions of extreme high and low ambient temperatures.This will be the material for our further research, as well as the influence of climate in the barn on the physiological state and productivity of dairy cows.+5.6 -20.5 +4.5 11 12.2 +1.2 12.0 +1.0 -24 -18.5 +5.5 -19.6 +4.4 12 13.1 +1.1 12.9 +0.9 -23 -17.7 +5.3 -18.7 +4.3 13 13.9 +0.9 13.8 +0.8 -22 -16.8 +5.2 -17.8 +4.2 14 14.8 +0.8 14.7 +0.7 -21 -15.9 +5.1 -16.8 +4.2 15 15.7 +0.7 15.6 +0.6 -20 -15.0 +5.0 -15.9 +4.1 16 16.6 +0.6 16.5 +0.5 -19 -14.2 +4.8 -15.0 +4.0 17 17.5 +0.5 17.4 +0.4 -18 -13.3 +4.7 -14.1 +3.9 18 18.3 +0.3 18.3 +0.3 -17 -12.4 +4.6 -13.2 +3.8 19 19.2 +0.2 19.3 +0.3 -16 -11.5 +4.5 -12.3 +3.7 20 20.1 +0.1 20.2 +0.2 -15 -10.6 +4.4 -11.4 +3.6 21 21.0 0.0 21.1 +0.1 -14 -9.8 +4.2 -10.5 +3.5 22 21.8 -0.2 22.0 0.0 -13 -8.9 +4.1 -9.6 +3.4 23 22.7 -0.3 22.9 -0.1 -12 -8.0 +4.0 -8.7 +3.3 24 23.6 -0.4 23.8 -0.2 -11 -7.1 +3.9 -7.8 +3.2 25 24.5 -0.5 24.7 -0.3 -10 -6.2 +3.8 -6.9 +3.1 26 25.4 -0.6 25.6 -0.4 -9 -5.4 +3.6 -6.0 +3.0 27 26.2-0.8 26.5 -0.5 -8 -4.5 +3.5 -5.1 +2.9 28 27.1 -0.9 27.4 -0.6 -7 -3.6 +3.4 -4.2 +2.8 29 28.0 -1.0 28.3 -0.7 -6 -2.7 +3.3 -3.3 +2.7 30 28.9 -1.1 29.2 -0.8 -5 -1.9 +3.1 -2.4 -2.6 31 29.8 -1.2 30.1 -0.9 -4 -1.0 +3.0 -1.5 +2.5 32 30.6 -1.4 31.0 -1.0 -3 -0.1 +2.9 -0.6 +2.4 33 31.5 -1.5 31.9 -1.1 -2 0.8 +2.8 0.3 +2.3 34 32.4 -1.6 32.8 -1.2 -1 1.7 +2.7 1.2 +2.2 35 33.3 -1.7 33.7 -1.3 0 2.5 +2.5 2.1 +2.1 36 34.1 -1.9 34.6 -1.4 1 3.4 +2.4 3.0 +2.0 37 35.0 -2.0 35.5 -1.5 2 4.3 +2.3 3.9 +1.9 38 35.9 -2.1 36.4 -1.6 3 5.2 +2.2 4.8 +1.8 39 36.8 -2.2 37.3 -1.7 4 6.0 +2.0 5.7 +1.7 40 37.7 -2.3 38.2 -1.8
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".