New Range Design and Mitigation methods for Sustainable Training.
Bibliographic record
Abstract
For years, DRDC Valcartier has been involved in the understanding of the environmental impacts of live-fire training with munitions within military Range and Training Areas (RTAs). After the extensive characterization of most of the Canadian ranges and R&D projects aimed at understanding the fate and behaviour of energetic materials and their toxicity, issues related to specific military activities were identified. Over the course of our research, it was found that anti-tank range target areas and firing positions were highly contaminated by energetic materials and metals, that demolitions training ranges and grenades ranges showed significant RDX concentrations in the soils that resulted in groundwater contamination, that small arms stop butts and firing positions were heavily contaminated with lead, antimony and, propellant compounds. This paper will describe the efforts conducted to mitigate the negative impacts of training on military ranges through the use of innovative approaches developed by our research team. These approaches and new ways of designing ranges will be proposed to minimize or even remove impacts on the environment. New small-arms bullet catcher prototypes, two concepts for propellant burning tables, and a new design for demolitions and grenade ranges will be discussed.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".