Passive signatures concealed objects recorded by multispectral and hyperspectral systems in visible, infrared and terahertz range
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
Risks to the safety of public zones (generally available for people) are related mainly to the presence of hidden dangerous objects (such as knives, guns, bombs etc.) and their usage. Modern system for the monitoring of such zones attempt to detect dangerous tools using multispectral cameras working in different spectral ranges: the visible radiation, near, medium and long range infrared and recently also in terahertz range. In order to develop methods and algorithms to detect hidden objects it is necessary to determine the thermal signatures of such objects of interest. The laboratory measurements were conducted to determine the thermal signatures of dangerous tools hidden under various clothes in different ambient conditions. Cameras used for measurements were working in spectral range 0.6-12.5 µm. An infrared imaging Fourier transform spectroradiometer was also used, working in spectral range 7.7-11.7 µm. Analysis of registered thermograms and hyperspectral datacubes has yielded the thermal signatures for: two types of guns, two types of knives and home-made explosive bombs. The determined thermal signatures will be used in the development of method and algorithms of image analysis implemented in proposed monitoring systems.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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