Particulate Matter Quantification Via DINÉ (Digitally INtegrated Environmental) Arduino UNO R3 Platform for Environmental Quality, Safety, and Health on the Navajo Nation
Bibliographic record
Abstract
After the hardware-integration of an Arduino UNO and the PMSA003 particulate matter (PM) sensor, the Arduino UNO was programmed to count six PM diameters: 0.3, 0.5, 1.0, 2.5, 5, and 10 micrometers, for a fixed air volume of 0.1 Liters. Indoor PM data was gathered within 12 different locations at Navajo Preparatory School, Farmington, NM. Outdoor atmospheric PM data was then gathered in Tucson, Arizona, before and after various weather events, such as high winds (PM generating) and rain (PM scrubbing). Additionally, indoor and outdoor data gathered in West Virginia during heavy smoke from the 2023 Canadian forest fires. The final data was gathered throughout the four-corners region of the desert southwest. The output of the Arduino UNO included current, average, maximum, and minimum PM values for each particle size. The Arduino UNO also calculated the least-squares fit a negative-exponential model of the particulate matter as a function of count and particle size, and calculated the correlation “R” between the actual PM count data and the model. Correlations as high as 99.99% were achieved at a confidence of 99.95%. This will help to understand the PM problems on the Navajo Nation, which could include radioactive dust from over 500 abandoned uranium mines.
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.
How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".