LWIR polarization sensing: investigation of liquids and solids with MoDDIFS
Bibliographic record
Abstract
MoDDIFS (Multi-option Differential Detection and Imaging Fourier Spectrometer) is a DRDC Valcartier technology built around a differential Fourier Transform Infrared (FTIR) spectrometer optimized for optical subtraction in the long wave infrared (LWIR). MoDDIFS is a dual use hyperspectral prototype offering two fore-optics configurations: "long range", specialized for the detection of small quantities of gaseous substances, and "polarization", built to investigate liquids and powders spills. We report and present a preliminary analysis of a series of measurement tests made with the polarization configuration. The tests were performed under indoor and outdoor environments. Different liquid and solid substances were deposited on different types of surfaces. Many liquid targets and some solid materials produce a noticeable linearly-polarized signal, with a more or less characteristic spectral modulation. For the liquids, the behavior of the observed radiance spectrum seems more predictable when the liquid is thick, or when it is deposited at any thickness on non-absorbing and weakly-reflective substrates. The behavior of the radiance spectrum observed becomes more complex when a thin layer of the liquid is deposited on a smooth and strongly-reflective substrate, or on an absorbing substrate. The parameter chosen to analyze the relative amount of polarization is the degree of linear polarization. When its value is noticeable, the polarized hyperspectral radiance measurements bring additional information on both targets and the backgrounds, as compared to standard unpolarized hyperspectral measurements. The tests performed can then help assess the materials for which the detection and the identification will be improved with polarized measurements.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".