Distribution and Fate of Energetics on DoD Test and Training Ranges: Final Report
Bibliographic record
Abstract
Access to live-fire training ranges is vital in maintaining the readiness of our Armed Forces.Understanding the nature of residue deposition and fate is critical to ensuring sound management of ranges.The objective of this project was to characterize residues of high explosives on training ranges.Residues were evaluated by sampling on various types of ranges across the U.S. and Canada.Deposition from high-order and low-order detonations, demolition, including blow-in-place, was characterized.Environmental transport parameters were developed to support estimates of site-specific source terms for risk assessment and groundwater models.Protocols were developed for characterizing soils containing the highly distributed solid formulations typical of ranges.Results demonstrated that residues are specific to range activities.Demolition areas, loworder detonations sites, and firing positions have great potential for accumulation of residues.Demolition typically generates small areas of relatively high concentrations.Low-order detonations generate primarily large solid particles reflecting the predetonation composition.Artillery impact areas tend to have widely distributed, low concentrations.Firing positions may exhibit high concentrations of propellants.This project defines the characteristics, distribution, and potential environment transport of explosives residues on training ranges and provides a scientific basis for development of reasonable control measures.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".