Influence of surface roughness, volume diffusion and particle size in reflectance infrared spectroscopy
Bibliographic record
Abstract
In the field of Defence and Public Security, standoff detection is a useful tool to identify many threats, such as chemical warfare agents, toxic industrial compounds and other chemical substances under various physical states (gas, liquid and solid). Over the years, Defence Research and Development Canada (DRDC) - Valcartier Research Centre has built an extensive expertise in the field of hyperspectral standoff detection. Although the technology behind the detection instruments is well known, the interpretation of spectral signatures of materials can sometimes be difficult. In reflectance infrared spectroscopy of solids, the same molecule can yield different signatures depending on sample surface roughness and particle sizes as opposed to the transmission and absorption infrared spectroscopies for which the signatures are significantly less influenced by these parameters. In principle, molecules containing one ionic bond, such as NaCl, must not exhibit infrared peaks, since only covalent bonds are supposed to generate active bands in the thermal infrared region. However, experimental results show significant spectral features in reflectance infrared spectroscopy. In fact, in reflectance spectroscopy, the volume diffusion phenomenon can generate spectral features even if no fundamental vibration occurs in the molecule. An effort has been undertaken at DRDC to understand the phenomenology associated with these variations. This paper summarizes experimental results which emphasize the roles of surface roughness and particle size in the interpretation of reflectance infrared spectra of solid materials.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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 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".