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Record W1980038079 · doi:10.2134/jeq2009.0240

Coarse Particulate Matter Emissions from Cattle Feedlots in Australia

2010· article· en· W1980038079 on OpenAlexaff
S. M. McGinn, Thomas K. Flesch, Deli Chen, B. P. Crenna, O. T. Denmead, Travis Naylor, D. Rowell

Bibliographic record

VenueJournal of Environmental Quality · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of AlbertaAgriculture and Agri-Food Canada
FundersMeat and Livestock Australia
KeywordsParticulatesEnvironmental scienceEnvironmental chemistryEcologyChemistryBiology

Abstract

fetched live from OpenAlex

Open cattle feedlots are a source of air pollutants that include particular matter (PM). Over 24 h, exposure to ambient concentrations of 50 microg m(-3) of the coarse-sized fraction PM (aerodynamic diameter <10 microm [PM(10)]) is recognized as a health concern for humans. The objective of our study was to document PM(10) concentration and emissions at two cattle feedlots in Australia over several days in summer. Two automated samplers were used to monitor the background and in-feedlot PM(10) concentrations. At the in-feedlot location, the PM(10) emission was calculated using a dispersion model. Our measurements revealed that the 24-h PM(10) concentrations on some of the days approached or exceeded the health criteria threshold of 50 microg m(-3) used in Australia. A key factor responsible for the generation of PM(10) was the increased activity of cattle in the evening that coincided with peak concentrations of PM(10) (maximum, 792 microg m(-3)) between 1930 and 2000 h. Rain coincided with a severe decline in PM(10) concentration and emission. A dispersion model used in our study estimated the emission of PM(10) between 31 and 60 g animal(-1) d(-1). These data contribute to needed information on PM(10) associated with livestock to develop results-based environmental policy.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0380.002

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 head0.056
GPT teacher head0.352
Teacher spread0.296 · 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; both teacher heads agree on what is shown here.

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

Citations31
Published2010
Admission routes1
Has abstractyes

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