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Record W2078552271 · doi:10.3390/ani2010001

Greenhouse Gas Emissions from Three Cage Layer Housing Systems

2011· article· en· W2078552271 on OpenAlexafffundabout
Sébastien Fournel, Frédéric Pelletier, Stéphane Godbout, R. Lagacé, J.J.R. Feddes

Bibliographic record

VenueAnimals · 2011
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsUniversity of AlbertaUniversité LavalInstitut de Recherche et de Développement en Agroenvironnement
FundersAgriculture and Agri-Food CanadaMinistère de l'Agriculture, des Pêcheries et de l'Alimentation
KeywordsManureGreenhouse gasManure managementNitrous oxideEnvironmental scienceCarbon dioxideCageLiquid manureMethaneAnimal scienceEnvironmental engineeringWaste managementChemistryAgronomyEcologyEngineeringBiology

Abstract

fetched live from OpenAlex

Agriculture accounts for 10 to 12% of the World's total greenhouse gas (GHG) emissions. Manure management alone is responsible for 13% of GHG emissions from the agricultural sector. During the last decade, Québec's egg production systems have shifted from deep-pit housing systems to manure belt housing systems. The objective of this study was to measure and compare carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O) emissions from three different cage layer housing systems: a deep liquid manure pit and a manure belt with natural or forced air drying. Deep liquid manure pit housing systems consist of "A" frame layer cages located over a closed pit containing the hens' droppings to which water is added to facilitate removal by pumping. Manure belt techniques imply that manure drops on a belt beneath each row of battery cages where it is either dried naturally or by forced air until it is removed. The experiment was replicated with 360 hens reared into twelve independent bench-scale rooms during eight weeks (19-27 weeks of age). The natural and forced air manure belt systems reduced CO₂ (28.2 and 28.7 kg yr(-1) hen(-1), respectively), CH₄ (25.3 and 27.7 g yr(-1) hen(-1), respectively) and N₂O (2.60 and 2.48 g yr(-1) hen(-1), respectively) emissions by about 21, 16 and 9% in comparison with the deep-pit technique (36.0 kg CO₂ yr(-1) hen(-1), 31.6 g CH₄ yr(-1) hen(-1) and 2.78 g N₂O yr(-1) hen(-1)). The shift to manure belt systems needs to be encouraged since this housing system significantly decreases the production of GHG.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.589

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 head0.066
GPT teacher head0.250
Teacher spread0.183 · 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

Citations27
Published2011
Admission routes3
Has abstractyes

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