TEMPERATURE CAPTURE AND IMAGE PROCESSING SYSTEM: A CASE STUDY
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
This paper describes a technical solution to stop the spread of COVID-19 by creating a system consisting of a video camera with a thermal sensor connected to a web-based platform, which would help to manage to restrict access of people who have fever into a building. The main purpose of the project system is to measure body temperature, to detect and to recognize the person that has at the moment or had fever in the past 14 days, the registration in the database of both the fever and the person, and validate the access of the person in the building if it has a temperature below 37 degrees Celsius. The technical details as analysis and determination of the domain of interest, development of the new system design, functional and non-functional requirements, interface, project planning, technical specification and quality of the proposed solution are discussed. The proposed system aims to reduce the number of employees responsible for collecting the temperature, thus no longer exposing them to the risk of infection.
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