Effects of heated drinking water on the production responses of lactating Holstein and Jersey cows
Bibliographic record
Abstract
An experiment was conducted to evaluate the effect of heating the drinking water of lactating dairy cows in different ambient environments on the feed and water intake and milk yield and composition and hydration status of Jersey and Holstein cows. Eighteen cows were randomly assigned to either an ambient (7–15°C), or a continuously heated (30–33°C), drinking water treatment in a switchback design. The experiment was replicated four times [spring (24.4°C), summer (21.1°C), autumn (11.8°C), and winter (12.6°C)] in a tie-stall facility. Free water intake was 3.40–5.95% greater (P < 0.05) each time the heated versus ambient drinking water was supplied across all trials. Both breeds responded similarly. Feed intake was increased 4.47% (P < 0.001) when cattle were offered the heated water during the summer experiment. Milk yield was greater (P < 0.01) for the spring and summer (P < 0.05) experiments when cattle were consuming the ambient water treatment. Water treatment had no effect on milk components or hydration status. Cows consumed 40% of their daily water intake within 2 h of each milking and feeding time. The results of this experiment demonstrate that cows drank more warm water when offered, but the increase in free water intake did not influence milk yield. Key words: Drinking water temperature, milk yield, feed and water intake, dairy cattle
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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 | 0.000 | 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 | 0.001 |
| 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".