Transport of explosives I: TNT in soil and its equilibrium vapor
Bibliographic record
Abstract
Landmine detection is an important task for military operations and for humanitarian demining. Conventional methods for landmine detection involve measurements of physical properties. Several of these methods fail on the detection of modern mines with plastic enclosures. Methods based on the detection signature explosives chemicals such as TNT and DNT are specific to landmines and explosive devices. However, such methods involve the measurements of the vapor trace, which can be deceiving of the actual mine location because of the complex transport phenomena that occur in the soil neighboring the buried landmine. We report on the results of the study of the explosives subject to similar environmental conditions as the actual mines. Soil samples containing TNT were used to study the effects of aging, temperature and moisture under controlled conditions. The soil used in the investigation was Ottawa sand. A JEOL GCMate II gas chromatograph ñ mass spectrometer coupled to a Tunable Electron Energy Monochromator (TEEM-GC/MS) was used to develop the method of analysis of explosives under enhanced detection conditions. Simultaneously, a GC with micro cell 63Ni, Electron Capture Detector (μECD) was used for analysis of TNT in sand. Both techniques were coupled with Solid-Phase Micro Extraction (SPME) methodology to collect TNT doped sand samples. The experiments were done in both, headspace and immersion modes of SPME for sampling of explosives. In the headspace experiments it was possible to detect appreciable TNT vapors as early as 1 hour after of preparing the samples, even at room temperature (20 °C). In the immersion experiments, I-SPME technique allowed for the detection of concentrations as low as 0.010 mg of explosive per kilogram of soil.
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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 | 0.001 |
| 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".