Carbon dioxide concentrations, temperature, and broiler chicken performance in a Canadian Prairie climate
Bibliographic record
Abstract
Carbon dioxide concentrations, indoor and outdoor temperature data were collected from 32 broiler barns and 15 observation sites across Southern Manitoba to better understand typical values observed during broiler production cycles. Individual dataset averages ranged from 421 to 4912 ppm of carbon dioxide. Monthly averages ranged from 1133 ppm to 3722 ppm. Monthly averages between November 2019 and March 2020 were all greater than 3000 ppm. 73 of 217 had averages greater than 3000 ppm, and 65 of these datasets occurred between November 2019 and March 2020. Of the 80 datasets collected between November 2019 and March 2020, 65 averaged over 3000 ppm. The data shows that carbon dioxide concentrations are closely related to outdoor temperatures on both an hourly and average level. This observation indicates that the fossil fuel combustion by the heating systems in the barns contribute significantly to the carbon dioxide concentrations in the barns. The observation was then proven using correlation and statistical analysis. Broiler performance parameters from two barns were obtained to relate the collected environmental data to broiler performance. The two broiler rooms were located on the same observation site and were of identical construction. Using JMP 16 analysis software (https://onthehub.com/), no statistically significant relationships at a 10% level between growth performance and environmental parameters were observed. Statistically significant relationships were observed when livability and condemnations were compared against the environmental data. Confounding variables were identified when relating performance parameters to carbon dioxide and the temperature difference between ambient conditions and the indoor barn temperature. The primary driver of the performance parameter trends cannot be made with this research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".