Bibliographic record
Abstract
Ionizing radiation, such as X-rays, is potentially harmful to humans. Ionizing radiation can be detected by radiation detectors, which are not easily available to the public. Thus, the feasibility of using smartphones to detect and measure X-ray exposures was investigated in this work. Two sets of experiments were conducted using an Apple iPhone 4 smartphone. For one experiment, the smartphone was used as an X-ray source, while the second experiment tested the use of the iPhone as an exposure meter. Using the iPhone 4, it was found that when videos were taken during X-ray exposures, white tracks appeared in the videos, which indicated a radiation absorption event. By counting the total number of tracks in the videos (using image processing software), X-ray exposures could be determined using a calibration factor obtained from the first set of experiments. It was found that the calibration factor was strongly dependent on the video settings, but weakly dependent on the incident angle of X-rays on the phone as long as the incident angle was within ±45 degrees from the normal incidence. It was observed that, as an exposure meter, the iPhone 4 was ±20% accurate compared to a standard detector used by hospitals. The results of this work suggest that it is feasible to use an iPhone 4 to measure radiation exposures.Les rayonnements ionisants comme les rayons X, peuvent être nuisible sans être sensiblement distingués par des humains. La faisabilité de l’utilisation des smartphones qui peuvent détecter des rayons X, et ce, en mesurant l’exposition à de tels rayons faisait l’objet de cet étude. Deux séries d’expériences ont été fait avec un iPhone4. Une série portait sur le calibrage de l’iPhone avec une source de rayon X. L’autre série portait sur l’utilisation de l’iPhone comme dispositif de photométrie. L’expérience a révélé que lors de la prise de vidéo pendant une exposition aux rayons X, des brillantes traces blanches se sont apparues dans les vidéos dont chacune a indiqué un événement d’absorption de radiation. En comptant le nombre total de traces dans les vidéos (utilisant un logiciel de traitement d’image), des expositions radiographiques pourraient être déterminées en utilisant un facteur de calibrage obtenu de la première série d’expériences. Les paramètres de vidéo ont eu une importante influence sur le facteur de calibrage, tandis que l’influence de l’angle d’incident de radiographies au téléphone leur signifiait moins tant que l’angle d’incident était d’environ ±45 degrés de l’incidence normale. L’iPhone comme dispositif de photométrie révélait être d’environ ±20 % précis par rapport à un détecteur standard utilisé dans des hôpitaux.
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 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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".