Energetic Residues from the Detonation of IMX‐104 Insensitive Munitions
Bibliographic record
Abstract
Abstract The development of insensitive munitions by NATO countries is an ongoing effort. Less‐sensitive ingredients in both explosives and propellants will ensure the protection of deployed troops against an unwanted reaction to an external stimulus on the munitions stockpile. In the US Army, current efforts are directed towards the development of melt cast insensitive explosive formulations. Various formulations, mainly based on DNAN and NTO, have been developed and are now being fielded. Our research goal is to measure the deposition rate of energetics compounds from various insensitive munitions detonation scenarios. Our hypothesis is that the relative insensitiveness of these formulations leads to slightly higher deposition rates than conventional explosive formulations. This paper describes detonation residues research on mortar rounds containing IMX‐104 explosive. Analyses indicate that high‐order detonation residues are slightly greater for this formulation than for conventional munitions. However, blow‐in‐place detonations (BIPs) resulted in much higher residues deposition, indicating that a larger donor charge is required for efficient detonation. The highly soluble compound NTO was particularly problematic, with BIP deposition approaching 95 % of the original load. Toxicological studies of NTO are not finalized, leaving considerable uncertainty regarding the feasibility of approving these rounds for distribution.
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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".