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Record W2913956362 · doi:10.3390/ani9020048

Readily Available Water Access is Associated with Greater Milk Production in Grazing Dairy Herds

2019· article· en· W2913956362 on OpenAlexaff
Ruan R. Daros, José A. Bran, María José Hötzel, M.A.G. von Keyserlingk

Bibliographic record

VenueAnimals · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsUniversity of British Columbia
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsGrazingHerdPastureMilk productionBreedAnimal scienceBiologyAgronomy

Abstract

fetched live from OpenAlex

In this cross-sectional study, we measured the association between water provision and milk production on intensively managed small-scale grazing dairy herds. Farms (n = 53) were categorized according to water provision as follows: (1) Restricted—cows did not have access to a water trough while on pasture; and (2) Unrestricted—cows had free access to a water trough while on pasture. Herd main breed and feeding practices were included in a model to assess the effect of water provision category on farm average milk yield/cow/d. The effect of pasture condition and environmental variables on milk production were also assessed, however were not retained on the final model. Herds provided with unrestricted access to drinking water produced on average 1.7 L more milk per cow/d (p = 0.03) than herds with restricted access to drinking water. Predominantly Holstein herds produced 2.8 L more milk per cow/d (p < 0.01) than non-Holstein herds. Each extra kg of concentrate offered per day increased milk yield by 1.1 L/cow/d (p < 0.01). In conclusion, providing free access to drinking water while grazing was associated with greater milk production.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.220
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations19
Published2019
Admission routes1
Has abstractyes

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