UV Raman detection of 2,4-DNT in contact with sand particles
Bibliographic record
Abstract
Deep Ultra Violet Raman Spectroscopy (DUV-RS) is an emerging tool for vibrational spectroscopy analysis and can be used in Point Detection mode to detect explosive components of landmines and Improvised Explosive Devices (IED). Interactions of explosives with different substrates can be measured by using quantitative vibrational signal shift information of scattered Raman light associated with these interactions. In this research, grounds were laid for detection of explosives using UV-Raman Spectroscopy equipped with 244 nm laser excitation line from a 488 nm frequency doubled Coherent FreD laser. In other experiments, samples of 2,4-DNT were allowed to interact with Ottawa Sand and were studied using DUV-RS. Characteristic vibrational signals of energetic compounds were analyzed in the ranges: 400-1200 cm-1, 1200-1800 cm-1, and 2800-3500 cm-1. In addition these Raman spectra were compared with dispersive spectra that were acquired using Raman Microscopy equipped with 514.5 nm (VIS) 785 nm (NIR) and 1064 nm (NIR) excitation lasers.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".