Assessments and application of low-cost sensors to study indoor air quality in layer facilities
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it