Near infrared imaging for multi-polar civilian applications
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
Infrared can be referred to any type of invisible electromagnetic spectrum having radiation wavelengths above the visible band and below the microwave band. We can define the near-infrared (NIR-approximately from 0.78 to 2.2 m) as the band located between the visible and the mid-wave infrared (MWIR approximately from 3 to 5 m).Nowadays, there are many applications where the NIR band is used. Some of them are biometrics, face recognition, surveillance and security, and biotechnology, among many others. In this paper, we present some of these applications using two NIR cameras: (1) a highend scientific CMOS camera made by Goodrich (0.9 to 1.7 m); and (2) a standard CCD camera made by Mutech (Phoenix model) (0.75 to 1.1 m) from which the NIR spectral filter has been removed to allow NIR radiation measurement. We have used both transmission and reflection modes to acquire the NIR data. A set of narrow-band spectral filters in order to optimize the signal for multispectral analysis.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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