Characterization and Fate of Gun and Rocket Propellant Residues on Testing and Training Ranges
Bibliographic record
Abstract
Abstract : Over the past two years, the U.S. Army Engineer Research and Development Center and the Defence R&D Canada Valcartier have partnered to develop an improved understanding of the distribution and fate of propellant residues on military training ranges in SERDP Project ER-1481. As a portion of this work, field studies have been conducted to estimate the mass of propellant residues deposited per round fired from various munitions. This research included artillery, mortars, small arms, shoulder-fired rockets, and several large missiles. Particles of the propellant residues deposited have been collected and studied, and initial experiments conducted to measure the rate of release of nitroglycerin (NG) and 2,4-dinitrotoluene (DNT) after deposition. Field studies have been conducted at a number of U.S. and Canadian installations to determine the mass and distribution of residue accumulation from different types of munitions. Depth profiling has been accomplished to document the depth to which these residues have penetrated the shallow subsoil. Laboratory column studies have been conducted with NG, nitroguanidine, and diphenylamine to document transport rates for solution phase propellant constituents and develop process descriptors for use in mathematical models to enable prediction of fate and transport for these constituents. Subsequent column studies have utilized intact propellants. The major accomplishments from these field and laboratory studies are presented.
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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 | 0.000 |
| 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".