Near infrared imaging for multi-polar civilian applications
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 µm) as the band located between the visible and the mid-wave infrared (MWIR approximately from 3 to 5 µm).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 µm); and (2) a standard CCD camera made by Mutech (Phoenix model) (0.75 to 1.1 µm) 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".