The effectiveness of smartphone temperature sensors for ambient temperature monitoring
Bibliographic record
Abstract
Background: Heat-related illness during extreme weather events is a leading cause of death and morbidity among vulnerable populations. Heat health alert systems are crucial in preventing serious impacts due to extreme heat, however its efficacy is limited by available atmospheric temperature data. A study was conducted to determine the accuracy of a silicon band-gap sensor integrated into certain models of smartphones when compared to a well-documented thermistor themperature sensor. Methods: Ambient temperature readings were taken at a location chosen within Burnaby, BC, using both a Met One sensor and a Sensirion sensor integrated into a smartphone. The data was then analyzed using a dependent T-test for paired samples to determine whether there was a significant difference between the grouped readings. Results: According to the results of the dependent T-test with data adjusted to a calibration curve, it was determined that there was no difference between the readings taken by the Met One and the Sensirion sensors, t(30)= -0.68, p=0.5 (95% CI, -0.04 to 0.02). Conclusions: Although further research is needed, the results of this study suggest that temperature sensors found in smartphones may be a smaller, lower-cost, and more accessible alternative to some of the higher-end models currently used to measure ambient temperature for the purposes of public health planning and policy-making.
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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 | 0.020 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".