Identity and distribution of residues of energetic compounds at military live-fire training ranges
Bibliographic record
Abstract
Environmental stewardship of military training ranges is an important objective of the Department of Defense. Therefore, an understanding of the explosives residues resulting from military training with various weapon systems is critical to range managers. A series of field sampling experiments was conducted at 27 military firing ranges in the United States and Canada to provide information on the identity and distribution of energetic munitions constituents. Different types of ranges were studied, including hand grenade, antitank rocket, artillery, bombing, and demolition ranges. Both firing points and impact areas were studied. Energetic compounds (explosives and propellants) were determined and linked to the type of munition used and the major mechanisms of deposition. At impact areas, the largest deposition of residues of energetic compounds is due to low-order detonations, or, in some cases, munitions that split open upon impact. The major residue deposited and its distribution varies for different types of ranges based upon the composition of the high explosive present in the warheads of the rounds fired at that type of range. For antitank range impact areas, the major residue present is HMX from the octol explosive used in the M72 66-mm LAW rockets. At artillery range impact areas, the major residues are TNT and/or RDX from the military-grade TNT and Composition B used in warheads of artillery and mortar rounds. Residues are very heterogeneously distributed at artillery range impact areas and can be described as randomly distributed point sources. RDX and TNT are the major residues at hand grenade ranges and their distribution is less heterogeneous due to the large number of individual detonations in a smaller area that further disperses the residues over the surface and at shallow depths. TNT is the major energetic compound detected at bombing ranges due to its presence in tritonal, the most common explosive used in bombs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".