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Record W4414342020 · doi:10.13031/ja.16383

Adaptation and Validation of the CO2 Balance Method for Ventilation Rate Estimation in Laying Hen Houses

2025· article· en· W4414342020 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of the ASABE · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsnot available
Fundersnot available
KeywordsLayingVentilation (architecture)Respiratory quotientBalance (ability)Production (economics)Adaptation (eye)Resource (disambiguation)

Abstract

fetched live from OpenAlex

Highlights Adapted CO2 balance improves ventilation rate prediction in poultry housing systems. Respiration quotient set to 0.9 (day) and 0.85 (night) enhances model accuracy. Adjusting the CO2 production rate (+18% day, +8% night) boosts model performance. Model accuracy declines with inlet-to-exhaust CO2 differences below 150 ppm. ABSTRACT. Assessing the environmental impact of poultry farms requires accurately determining ventilation rates (VR) while minimizing resource use. Accurate VR estimates are crucial for precisely evaluating emissions, making VR a vital factor in enhancing the sustainability of poultry production. This study aims to adapt and validate the carbon dioxide (CO 2 ) balance method for VR prediction in laying hen houses. The adaptation was based on the CO 2 balance method from the International Commission of Agricultural and Biosystems Engineering (CIGR), which considers variables such as the CO 2 production rate of hens (PRCO 2 ), animal activity (AA), and the respiratory quotient of laying hens (RQ). Data collected from an experiment involving laying hens in conventional cages (CC) under laboratory conditions were used to adapt the VR prediction model. The adapted model was validated under both laboratory and commercial conditions in three housing systems: conventional cages (CC), enriched cages (EC), and cage-free systems (CF). At the laboratory level, validation was performed using data from two studies conducted in the same controlled laboratory conditions but with different housing systems (the first in CF and the second in the three systems simultaneously). At the commercial level, validation was performed using data collected from 30 commercial farms in Quebec, Canada. The resulting adaptations included setting AA to 1, RQ to 0.9 during the day and 0.85 at night, and PRCO 2 to 18% during the day and 8% at night. The adjusted model demonstrated an R 2 of 0.63 for VR prediction when the difference between exhaled and inlet CO 2 was less than 150 ppm. At the experimental level, all evaluated housing systems showed an R 2 greater than 0.63 and an average RMSE of 0.35 m 3 h -1 hen -1 . At the commercial level, housing systems exhibited an average predicted R 2 of 0.71 and an RMSE of 1.68 m 3 h -1 hen -1 . The adapted CO 2 balance method presented good predictive values across laboratory and commercial experiments. Keywords: Airflow, Animal activity, Egg production, Gas production, Poultry building, Respiratory quotient.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.742
Threshold uncertainty score0.049

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 head0.020
GPT teacher head0.268
Teacher spread0.248 · 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