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Record W2036890777 · doi:10.1071/an14550

Correlations of methane and carbon dioxide concentrations from feedlot cattle as a predictor of methane emissions

2014· article· en· W2036890777 on OpenAlexaff
Mei Bai, David Griffith, Frances Phillips, Travis Naylor, S. K. Muir, S. M. McGinn, Deli Chen

Bibliographic record

VenueAnimal Production Science · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsAgriculture and Agri-Food Canada
FundersUniversity of MelbourneUniversity of Wollongong
KeywordsMethaneGreenhouse gasCarbon dioxideFeedlotEnvironmental scienceTemperate climateEnvironmental chemistryBeef cattleAnimal scienceChemistryAgronomyEcologyBiology

Abstract

fetched live from OpenAlex

Accurate measurements of methane (CH4) emissions from feedlot cattle are required for verifying greenhouse gas (GHG) accounting and mitigation strategies. We investigate a new method for estimating CH4 emissions by examining the correlation between CH4 and carbon dioxide (CO2) concentrations from two beef cattle feedlots in Australia representing southern temperate and northern subtropical locations. Concentrations of CH4 and CO2 were measured at the two feedlots during summer and winter, using open-path Fourier transform infrared spectroscopy. There was a strong correlation for the concentrations above background of CH4 and CO2 with concentration ratios of 0.008 to 0.044 ppm/ppm (R2 >0.90). The CH4/CO2 concentration ratio varied with animal diet and ambient temperature. The CH4/CO2 concentration ratio provides an alternative method to estimate CH4 emissions from feedlots when combined with CO2 production derived from metabolisable energy or heat 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 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 score0.660
Threshold uncertainty score0.639

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 studies0.0000.002
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 head0.010
GPT teacher head0.230
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

Citations8
Published2014
Admission routes1
Has abstractyes

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