Effect of training dairy heifers to an automated milking system before parturition on their adaptation and performance
Bibliographic record
Abstract
The objective of this study was to determine how training dairy heifers (first-lactation cows before their first calving) on an automated milking system (AMS) before parturition affects their adaptation and performance when milked on the AMS. Thirty pregnant Holstein dairy heifers were enrolled in the study 23 ± 5.2 d (mean ± SD) before their expected calving date in dry cow pens located away from the AMS. Heifers were paired by expected calving date and allocated into 1 of 2 treatments within pair (16 pairs initially enrolled): control (CON; no training; n = 16 heifers completed) and training (TRN; this included exposure to AMS environment, concentrate, and mechanical arm and noises; n = 14 heifers completed). The TRN heifers were trained to the AMS over 4 d, 14 ± 5.1 d before their actual calving date, with 3 training sessions/d. All heifers calved in individual maternity pens and were then moved to the free-traffic AMS pen between 3 and 7 DIM to be milked by the AMS, with a fetch pen adjacent to the entrance of the AMS. Milking activity (visits, milkings), behavior (feeding, rumination), and production were recorded for the first 21 d on the AMS. Ease of entry was scored from 0 to 6 (0 = no assistance to 6 = heavy assistance), kicking from 0 to 3 (0 = no kicking to 3 = >2 kicking events), and milk letdown from 0 to 3 (0 = normal letdown to 3 = no letdown) for the first 6 milkings on the AMS across the first 2 d. Time spent around the AMS was recorded on d 1, 3, 7, 14, and 21 of milking on the AMS, and any time spent in the fetch pen was recorded for all 21 d. The TRN cows had a better (lower) ease of entry score (2.87 vs. 4.26) and milk letdown score (0.35 vs. 0.70) during the first 6 milkings. The TRN cows had more total visits to the AMS (6.1/d vs. 5.0/d), voluntary visits (5.6/d vs. 4.2/d), and voluntary milkings (2.6/d vs. 2.2/d) on the AMS across the 21-d period, compared with CON cows. The CON cows had more fetches (1.0/d vs. 0.8/d) to the AMS across the 21-d period and spent more time in the fetch pen (18.7 min/d vs. 14.6 min/d) compared with TRN cows. The improved adaptation for TRN cows may have contributed to a higher milk yield (32.8 kg/d vs. 30.6) compared with CON cows. Overall, these results demonstrate that training before calving improved adaptation and performance of cows milked on an AMS compared with cows without previous exposure to the AMS, and its concentrate and mechanics exposure within, before their first AMS milking.
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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 | 0.000 | 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".