AUTOMATIC CONCENTRATE DISTRIBUTION FOR FATTENING OF ROMANOV × DORPER LAMBS
Bibliographic record
Abstract
The aim of this research was to study the possibility of using automatic concentrate feeding stations in fattening of lambs. Ten Romanov × Dorper weaned male lambs (initial live weight 21.0 ± 0.86 kg) for fattening were used. Lambs were kept indoors in separate pen and research was carried out in production conditions. Concentrate was distributed for animals individually in automatic feeding station. Adaption period were not applied, eight lambs had the concentrate intake in the automatic feeding station from first research day, one started eat concentrate from third research day and one – from eleventh day of research. The frequency of visits to automatic feeding station and daily concentrate intake was recorded and analyzed. Lamb’s were weighted before research and every fourteen days, live weight changes were analyzed. During all the research average number of daily visits to automatic feeding station of one lamb were 13 visits, average daily concentrate intake per animal was: 84 % of the average ration (1642 g) in all research period. Results shows, that average daily live weight gain was 246 ± 26.3 g, during last quarter daily live weight gain (89 ± 27.7 g) was significantly (p < 0.05) lover than in other quarters. For 1 kg lamb live weight gain 5.39 kg concentrate was used.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".