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Record W2151997827 · doi:10.1071/ea07204

Methane emissions from feedlot cattle in Australia and Canada

2008· article· en· W2151997827 on OpenAlexaffabout
S. M. McGinn, Deli Chen, Zoë Loh, J. Hill, K. A. Beauchemin, O. T. Denmead

Bibliographic record

VenueAustralian Journal of Experimental Agriculture · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
FundersJohnson and Johnson
KeywordsFeedlotEnvironmental scienceGreenhouse gasAnimal scienceStockingBeef cattleMethaneBiologyEcology

Abstract

fetched live from OpenAlex

Raising beef cattle in open feedlots is a well established practice in Canada and is gaining acceptance in Australia because it results in more consistent meat quality. These facilities are regional ‘hot spots’ of methane (CH4) emissions, resulting from the high stocking density and the large amount of fermentation occurring in the rumen (enteric CH4). Our objective was to compare CH4 emissions from a typical feedlot in Australia (Queensland) and in Canada (Alberta) and also to compare these against modelled emissions. Methane concentration and wind data were monitored over a portion of each feedlot and a dispersion model was used to calculate CH4 emissions during a summer period. The average CH4 emission was 166 ± 90 and 214 ± 61 g/animal.day for the feedlot in Queensland and in Alberta, respectively. The lower CH4 emission at the Queensland feedlot was attributed to the lighter weight of the cattle, and consequently their lower intake, and supplementation of the diet with lipids. The lipid effect on CH4 emissions is also speculated to cause some models to overestimate the measured CH4 emissions. A lower CH4 emission also occurred during daylight hours at the Queensland feedlot and was attributed in part to heat stress as defined by the temperature–humidity index.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score0.827

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.037
GPT teacher head0.258
Teacher spread0.221 · 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

Citations54
Published2008
Admission routes2
Has abstractyes

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