Interference Factors and Compensation Methods When Using Infrared Thermography for Temperature Measurement: A Review
Why this work is in the frame
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Bibliographic record
Abstract
Infrared thermography (IRT) is a widely used temperature measurement technology, but it faces the problem of measurement errors under interference factors. This article attempts to summarize the common interference factors and temperature compensation methods when applying IRT. According to the source of factors affecting the infrared temperature measurement (ITM) accuracy, the interference factors are divided into three categories: factors from the external environment, factors from the measured object, and factors from the infrared thermal imager itself. At the same time, the existing compensation methods are summarized and classified into three categories: mechanism modeling-based compensation (MMC) method, data-driven compensation (DDC) method, and mechanism and data jointly driven compensation (MDC) method. Furthermore, we discuss the problems existing in the temperature compensation methods and future research directions, aiming to provide some References for researchers in academia and industry when using IRT technology for temperature measurement.
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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 | 0.000 |
| 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