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Record W2798049181 · doi:10.3390/atmos9040139

A Technique for Estimating Greenhouse Gas Exchange Adjacent Cattle Feedlots

2018· article· en· W2798049181 on OpenAlexafffund
S. M. McGinn, Thomas K. Flesch

Bibliographic record

VenueAtmosphere · 2018
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsUniversity of AlbertaAgriculture and Agri-Food Canada
FundersAlberta Livestock and Meat Agency
KeywordsEnvironmental scienceGreenhouse gasPlumeNitrous oxideMethaneCarbon dioxideEnvironmental engineeringFeedlotHydrology (agriculture)MeteorologyChemistry

Abstract

fetched live from OpenAlex

Concentrated animal feeding operations (CAFO) such as open-air beef cattle feedlots are known ‘hot spots’ of emissions of numerous gases including the major greenhouse gases methane, nitrous oxide, and carbon dioxide. Some work has documented CAFOs to derive typical emission factors, but few studies have looked beyond the CAFO to the local landscape to derive the net off-farm emissions. To address the net emissions, the exchange of gases downwind of CAFOs is required, determined in part by the air quality of the gas plume from the CAFO and the characteristics of the underlying surface. Our study measured these downwind fluxes at an open-air beef cattle feedlot using an open-path Fourier Transform Infrared detector and a flux-gradient method. The results showed the dynamic response of fluxes to gas concentration (fresh air or feedlot air) and surface condition (actively growing crop and tilled stubble). These results shed light on the pathways of greenhouse gas flow near a CAFO source, and showed that solely measuring source emissions from a CAFO would lead to errors when developing emission factors.

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: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.612
Threshold uncertainty score0.609

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)0.0000.000

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 head0.019
GPT teacher head0.261
Teacher spread0.242 · 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
GenreMethods

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

Citations2
Published2018
Admission routes2
Has abstractyes

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