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Record W2110589012 · doi:10.1071/ea07244

Measurement of greenhouse gas emissions from Australian feedlot beef production using open-path spectroscopy and atmospheric dispersion modelling

2008· article· en· W2110589012 on OpenAlexaff
Zoë Loh, Deli Chen, Mei Bai, Travis Naylor, David Griffith, J. Hill, Tom Denmead, S. M. McGinn, Robert Edis

Bibliographic record

VenueAustralian Journal of Experimental Agriculture · 2008
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsAgriculture and Agri-Food Canada
FundersMeat and Livestock Australia
KeywordsFeedlotGreenhouse gasEnvironmental scienceBeef cattleMethaneDispersion (optics)Atmospheric sciencesAtmospheric dispersion modelingHydrology (agriculture)Animal scienceAir pollutionEcologyBiologyPhysics

Abstract

fetched live from OpenAlex

Feedlot production of beef cattle results in concentrated sources of gas emissions to the atmosphere. Reported here are the preliminary results of a micrometeorological study using open-path concentration measurements to determine whole-of-feedlot emissions of methane (CH4) and ammonia (NH3). Tunable near-infrared diode lasers were used to measure line-averaged (150–400 m) open-path concentrations of CH4 and NH3. A backward Lagrangian stochastic model of atmospheric dispersion and the software package WindTrax were used to estimate greenhouse gas fluxes from the measured concentrations. We studied typical Australian beef feedlots in the north (Queensland) and south (Victoria) of the continent. The data from a campaign during summer show a range of CH4 emissions from 146 g/animal.day in Victoria to 166 g/animal.day in Queensland and NH3 emissions from 125 g/animal.day in Victoria to 253 g/animal.day Queensland.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.051
GPT teacher head0.270
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations73
Published2008
Admission routes1
Has abstractyes

Explore more

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