Multivariate analysis for data mining to characterize poultry house environment in winter
Bibliographic record
Abstract
The processing and analysis of massive high-dimensional datasets are important issues in precision livestock farming (PLF). This study explored the use of multivariate analysis tools to analyze environmental data from multiple sensors located throughout a broiler house. An experiment was conducted to collect a comprehensive set of environmental data including particulate matter (TSP, PM10, and PM2.5), ammonia, carbon dioxide, air temperature, relative humidity, and in-cage and aisle wind speeds from 60 locations in a typical commercial broiler house. The dataset was divided into three growth phases (weeks 1–3, 4–6, and 7–9). Spearman's correlation analysis and principal component analysis (PCA) were used to investigate the latent associations between environmental variables resulting in the identification of variables that played important roles in indoor air quality. Three cluster analysis methods; k-means, k-medoids, and fuzzy c-means cluster analysis (FCM), were used to group the measured parameters based on their environmental impact in the broiler house. In general, the Spearman and PCA results showed that the in-cage wind speed, aisle wind speed, and relative humidity played critical roles in indoor air quality distribution during broiler rearing. All three clustering methods were found to be suitable for grouping data, with FCM outperforming the other two. Using data clustering, the broiler house spaces were divided into three, two, and two subspaces (clusters) for weeks 1–3, 4–6, and 7–9, respectively. The subspace in the center of the house had a poorer air quality than other subspaces.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".