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Record W2000624740 · doi:10.4141/a01-055

Effects of heated drinking water on the production responses of lactating Holstein and Jersey cows

2002· article· en· W2000624740 on OpenAlexafffundvenue
V.R. Osborne, R. R. Hacker, B.W. McBride

Bibliographic record

VenueCanadian Journal of Animal Science · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaDairy Farmers of Ontario
KeywordsMilkingAnimal scienceWater intakeMilk productionDairy cattleChemistryBiologyEnvironmental scienceEnvironmental engineering

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.202
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations40
Published2002
Admission routes3
Has abstractyes

Explore more

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