Assessments and application of low-cost sensors to study indoor air quality in layer facilities
Bibliographic record
Abstract
Indoor poultry facilities often experience poor air quality due to intensive farming and restricted ventilation. Monitoring the air quality in these barns is crucial considering the health of both the birds and producers. Advancements in sensor technologies have led to the development of low-cost sensors (LCS) that can continuously monitor air pollutants. Even though most poultry facilities in Canada are indoors due to harsh winter weather conditions, there is a lack of indoor air quality (IAQ) studies. This study aimed to evaluate the field performance of the LCS network in a table egg farm in Canada, where the sensors were designed specifically for operating in dusty poultry facilities continuously. The LCS monitored IQA parameters such as particulate matter (PM), carbon dioxide (CO2), relative humidity, and temperature in real-time. By implementing a correction factor, the sensor data resulted in an agreement range of 80 ± 20% with a reference instrument. The study observed that PM concentration exceeded several thousand μg/m3, with PM10 at 5.5 × 104 ± 2.2 × 104 and PM2.5 at 6.3 × 103 ± 2.3 × 103, which was found to be most affected by the chicken activity and light regime. The IAQ parameters also exhibited a complex intercorrelation with each other, as well as the outdoor temperature and the building ventilation rate. Sensors were able to make observations that were found only with research-grade instruments in previous studies. Overall, the study showcases the potential of the LCS network as an affordable solution for environmental monitoring in poultry facilities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".