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Record W1791261215 · doi:10.4141/cjas2012-072

Methane emissions from a swine manure tank in western Canada

2012· article· en· W1791261215 on OpenAlexaffvenueabout
Thomas K. Flesch, X.P.C. Vergé, R. L. Desjardins, Devon E. Worth

Bibliographic record

VenueCanadian Journal of Animal Science · 2012
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Alberta
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsManureEnvironmental scienceMethaneMethane emissionsManure managementAnimal scienceEnvironmental engineeringAtmospheric sciencesAgronomyChemistryPhysicsBiology

Abstract

fetched live from OpenAlex

Flesch, T. K., Vergé, X. P. C., Desjardins, R. L. and Worth, D. 2013. Methane emissions from a swine manure tank in western Canada. Can. J. Anim. Sci. 93: 159–169. The emission rate of methane (CH 4 ) to the atmosphere was measured from a concrete manure tank at a farrow-to-finish swine facility in western Canada. Measurements were made during four seasonal campaigns using a bLS inverse-dispersion technique. Emission rates were highest in summer and lowest in winter, with intermediate rates in spring and fall. Annual emissions were estimated at 7600 kg CH 4 , or 6.3 kg CH 4 m −2 of tank surface area. Site-specific factors used for estimating CH 4 emissions were calculated from our measurements. A simple methane conversion factor, used by the Intergovernmental Panel on Climate Change to relate emissions to the volatile solids content of the manure, was calculated as 0.23. This value may be unrepresentatively high due to the long duration (15 mo) that manure was stored in the tank. A more sophisticated calculation methodology considers the influence of manure storage duration and temperature, and includes a critical management design practices (MDP) factor. The MDP factor was calculated as 0.31 for our tank. This MDP value implies that emissions from our manure tank were lower than expected given the results from other studies.

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)0.001
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 score0.689
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
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 head0.018
GPT teacher head0.242
Teacher spread0.224 · 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

Citations42
Published2012
Admission routes3
Has abstractyes

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