Heat source selection for drone-based active-infrared thermography
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
A drone-based inspection system that can fly, hover, and navigate around structures to perform the inspection in an efficient/fast manner can considerably reduce inspection time. Active thermography is a well-known non-destructive testing method for inspection. However, using it on a drone is challenging due to the drone needing to carry an appropriate heat source, batteries or tethering system to power the heat source and to provide adequate flight time. This complicates the inspection process and can restrict the amount of thermal energy that can be applied to the inspected structure. Another challenge with drone-based active infrared thermography (DBAIT) is that, unlike traditional active thermography inspection in which, the source is either stationary or moving in a precisely controlled manner, the drone and the heat source are subjected to undesired dynamic motion. This paper presents the results of experiments performed to compare potential heat sources that can be retrofitted onboard a drone to conduct active thermographic inspection.
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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 | 0.000 | 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