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Record W2116236844 · doi:10.1071/an14152

Including essential oils in lactating dairy cow diets: effects on methane emissions1

2014· article· en· W2116236844 on OpenAlex
Sarah J. Meale, Alex V. Chaves, Tim A. McAllister, A. D. Iwaasa, Wenzhu Yang, C. Benchaar

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnimal Production Science · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsAnimal scienceGarlic OilJuniperChemistryLatin squareDairy cattleBerryMonensinFood scienceBiologyRumenBotanyFermentation

Abstract

fetched live from OpenAlex

The objective of this study was to examine the effects of dietary supplementation of garlic and juniper berry essential oils on methane (CH4) and carbon dioxide (CO2) emissions from lactating dairy cows. Four ruminally cannulated, lactating Holstein cows were used in a 4 × 4 Latin square (21-day period; 11 days of adaptation). Cows were fed a total mixed ration (60 : 40 forage : concentrate ratio) without supplementation (no additive; negative control) or supplemented with monensin (330 mg/day; positive control), garlic oil (5 g/day) or juniper berry oil (2 g/day). Methane and CO2 emissions were measured using the sulfur hexafluoride tracer technique. Dietary supplementation of lactating cows with juniper berry oil or garlic oil did not affect (P > 0.05) CH4 or CO2 production, whether expressed as g/day, g/kg DMI, g/kg milk or as g/kg DMI/BW0.75. At the doses administered in this study, the anti-methanogenic effect of garlic and juniper berry oils previously observed in vitro were not confirmed in vivo.

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.

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)0.003
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 score0.254
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.298
Teacher spread0.264 · 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