Spectroscopic signatures of PETN in contact with sand particles
Bibliographic record
Abstract
The detection of explosive materials is not only important as an issue in landmines but also for global security reasons, unexploded ordnance, and Improvised Explosive Devices detection. In such areas, explosives detection has played a central role in ensuring the safety of the lives of citizens in many countries. Raman Spectroscopy is a well established tool for vibrational spectroscopic analysis and can be applied to the field of explosives identification and detection. The analysis of PETN is important because it is used in laminar form or mixed with RDX to manufacture Semtex plastic explosive and in the fabrication of Improvised Explosive Devices (IEDs). Our investigation is focused on the study of spectroscopic signatures of PETN in contact with soil. Ottawa sand mixed in different proportions with PETN together with the study of the influence of pH, temperature, humidity, and UV light on the vibrational signatures of the mixtures constitute the core of the investigation. The results reveal that the characteristic bands of PETN are not significantly shifted but rather appear constant with respect of the ubiquitous band of sand (~463 cm-1). These results will make possible the development of highly sensitive sensors for detection of explosives materials and IDEs.
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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) | 0.002 | 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".