Milk yield relative to supplement intake and rumination time differs by health status for fresh cows milked with automated systems
Bibliographic record
Abstract
Automated milking systems (AMS) provide the capability of adjusting the amount of supplemental concentrate offered to cows based on parity, days in milk, and milk yield; in many situations, more supplement is provided to those cows with higher production demands. To determine associations of subclinical ketosis (SCK) and blood β-hydroxybutyrate (BHB) with milk yield, supplement consumed in the AMS, rumination time, and the ratios of milk yield to supplement intake and milk yield to rumination time, we monitored 605 cows from 9 AMS herds, testing blood BHB concentrations 1×/wk for the first 3 wk of lactation. Milk yield, supplement intake, and rumination data were collected from the AMS computer on each farm. For analyses, only multiparous cows from 8 herds were included (n = 172 total) and were matched, within farm, to include an equal number of cows in each of 4 health status groups (n = 43 per group): SCK (BHB ≥1.2 mmol/L at ≥1 of 3 tests, with no other disorder in the first 30 d in milk), SCK+ (BHB ≥1.2 mmol/L at ≥1 of 3 tests, with another health disorder), HLT (BHB always <1.2 mmol/L, with no other disorder), or OTH (BHB always <1.2 mmol/L, with a health disorder). Milk yield and rumination time varied by health status, but we found no difference in supplement intake by health status group. As a result, milk yield relative to supplement intake and milk yield relative to rumination time differed by health status and were both positively associated with BHB. Cows in SCK had the highest milk yield and ratio of milk yield to supplement intake, but SCK+ cows had the lowest rumination time and the highest milk yield to rumination ratio. These results highlight the differences in milk production (per day and relative to supplement consumption or rumination time) associated with blood BHB and health status. Therefore, potential exists to modify supplementation in AMS to reduce negative energy balance by accounting for milk production of fresh cows.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".