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Record W1973967085 · doi:10.1071/an14992

Non-interference measurement of CH4, N2O and NH3 emissions from cattle

2015· article· en· W1973967085 on OpenAlexaff
Mei Bai, Jianlei Sun, Kithsiri B. Dassanayake, M. A. Benvenutti, J. Hill, O. T. Denmead, Thomas K. Flesch, Deli Chen

Bibliographic record

VenueAnimal Production Science · 2015
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsUniversity of Alberta
FundersMeat and Livestock AustraliaDookie Campus, University of Melbourne
KeywordsNitrous oxideEnvironmental scienceMethaneMethane emissionsGreenhouse gasBeef cattleAnimal scienceIrrigationFeedlotAtmospheric sciencesAgronomyChemistryEcologyBiologyPhysics

Abstract

fetched live from OpenAlex

A technique combining open-path Fourier transform infrared spectroscopy with an inverse-dispersion model was used to quantify methane (CH4), nitrous oxide (N2O) and ammonia (NH3) emissions from an isolated cattle pen in south-eastern Australia. Twenty-eight Angus steers (1-year old, initial average liveweight 404 kg) were fed a 60% grain diet and kept in a pen (20 × 20 m) for 41 days. Gas concentrations were measured downwind of the pen using an open-path Fourier transform infrared spectroscopy with a path length of 100 m, having a detection sensitivity of 2, 0.3 and 0.4 ppb for CH4, N2O and NH3, respectively. Daily emission rates were 232, 14 and 192 g/cattle.day for CH4, N2O and NH3, respectively. The measured CH4 emissions were in agreement with predictions based on Australian National Inventory recommendations, however, measured N2O and NH3 emissions were much higher than the predicted values. Extrapolation of our measurements would mean that CH4 and N2O emissions from beef feedlot cattle contribute 3.1% and 5.9% of the Australian agricultural CH4 and N2O emissions, respectively.

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)0.001
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 score0.004
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

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

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 head0.073
GPT teacher head0.284
Teacher spread0.211 · 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

Citations12
Published2015
Admission routes1
Has abstractyes

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