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Record W4307993358 · doi:10.3389/fanim.2022.1029094

Understanding variability and repeatability of enteric methane production in feedlot cattle

2022· article· en· W4307993358 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFrontiers in Animal Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsAnimal scienceRumenFeedlotBeef cattleDry matterRepeatabilitySilageCrossbreedBiologyChemistryFermentationFood science

Abstract

fetched live from OpenAlex

Breeding ruminants for low methane (CH 4 ) emissions can be permanent and cumulative, but requires a better understanding of the variability of CH 4 production among animals to accurately assess low-CH 4 phenotypes. Our objectives were to: 1) investigate the variation in CH 4 production among and within growing beef cattle, 2) identify low-CH 4 emitters, and 3) examine relationships between CH 4 production and intake, feeding behavior, growth, and rumen fermentation. Crossbred beef heifers (n=77; body weight=450 kg) were allocated to 3 pens and offered a finishing diet of 90% concentrate and 10% silage (dry matter (DM) basis). The study was conducted over 3 consecutive 6-week periods (126 days). GrowSafe bunks measured individual animal DM intake (DMI) and rumen fluid was sampled orally each period. A GreenFeed system measured individual animal emissions for 2 weeks/period. Methane production was calculated by animal within period using visits that were ≥3 min with fluxes compiled into six 4-h blocks corresponding to time of day, and averaged over blocks to obtain an average daily emission for the period. Animals with <12 visits and <5 blocks were omitted for the period and animals with ≥2 periods of complete CH 4 data were used in the final analysis (n=52). Animals were ranked based on CH 4 yield (g/kg DMI) from low to high, and grouped as Very-low (≤10% of animals), Low (11-25%), Intermediate (26-74%), High (75-89%), and Very high (≥90%) emitters (mean ± SD, 12.6 ± 2.16). The CH 4 yield was 16% less ( P <0.05) for Very-low compared with Intermediate animals due to lower CH 4 production (g/d, P <0.05), with no differences in DMI ( P >0.05). However, the period × grouping interaction ( P <0.001) for CH 4 yield indicated that the ranking of animals changed over time, although there were no extreme changes in rankings. Total VFA concentration decreased as CH 4 yield decreased, but molar proportions of VFA remained unchanged, suggesting lower extent of ruminal digestion rather than a shift in fermentation. There were no differences in feeding behavior or average daily gain among groupings ( P >0.05). The between-animal coefficient of variation in CH 4 yield of 17.3% enabled identification of low CH 4 -emmitting finishing beef cattle. However, accurate selection of low CH 4 -emitting animals should be based on repeated CH 4 measurements over the production cycle.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.319
Threshold uncertainty score0.193

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.054
GPT teacher head0.251
Teacher spread0.197 · 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