Deposition of PETN Following the Detonation of Seismoplast Plastic Explosive
Bibliographic record
Abstract
Abstract Many nation’s armed forces are undertaking efforts to minimize the environmental impacts of live‐fire military training. Based on this, the Canadian Department of National Defence has undertaken a project to examine potential alternatives to the use of Composition C4, an RDX‐based plastic explosive. Plastic explosives are widely used by all armed forces for both military engineering tasks and explosive ordnance disposal and their use may lead to the deposition of explosives in the environment, namely RDX, in the case of C4. RDX is very stable in the environment, water soluble, and moves relatively rapidly towards surface and groundwater bodies. One option identified as a potential RDX‐free formulation is a pentaerythritol tetranitrate (PETN) based plastic explosive, commercially available in Germany and referred to as Seismoplast. In order to measure the environmental impacts of this formulation, a deposition rate study was conducted. These tests consist of evaluating the detonation efficiencies of munitions during detonation scenarios representative of military training. Data generated from these tests are the deposition masses of the energetic components in the explosive filler, which in this case is PETN. To achieve this objective, seven blocks of Seismoplast were open detonated over a surface of pristine snow, and post‐detonation surface samples were collected to measure residual PETN. The trial demonstrated that less than 1×10−7 % of PETN is deposited upon detonation of Seismoplast. The energetic material deposition rates obtained in this trial are much lower than rates obtained for the RDX‐based C4 currently in‐service within Canada. Switching from a RDX‐based plastic explosive to one based on PETN may be an interesting option through which the Department of National Defence can reduce the environmental impact of its activities.
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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".