Distribution and Fate of Energetics on DoD Test and Training Ranges
Bibliographic record
Abstract
The DoD has a mandate of environmental stewardship as well as military readiness. Therefore, the concern that training with live munitions potentially generates undesirable residual constituents is of interest. The objective of this study is to develop techniques for assessing the potential for environmental contamination from energetic materials on testing and training ranges. The project defines the physical and chemical properties, concentrations, and distribution of residues in soils, and the potential for transport of these residues to groundwater. Surface soils associated with impact craters, targets areas, and firing points were characterized on 18 military installations in the United States and Canada. Residues from high-order, low-order, unconfined charge, and blow-in-place detonations were collected on witness plates, snow, and/or tarps for constituent analyses. Results of these analyses were used to characterize residue composition and spatial distribution in relationship to the types of training activities conducted. Results also contributed to development of surface soil sampling strategies for live-fire ranges. Transport parameters of contaminants of potential concern for which data are lacking were determined by leveraging this project with other landing sources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".