Trials in Developing Mitigation Devices to Manage Munitions Constituents Release into the Environment on Live Fire Ranges
Bibliographic record
Abstract
Live fire military training is bound to release munition constituents (MC) in the environment, risking the contamination of surrounding lands. It is known that those releases can be deleterious to sensitive receptors on and outside military bases and pose conformity issues with regulatory bodies. The omnipresence of waterbodies on some installations worsens the probability of negative impact. The MC usually found to be problematic in live fire ranges are heavy metals (Pb, Cu, Zn, Sb) and energetic materials (RDX, HMX, TNT). For the past decade, Canadian army installations have been the scene for trials of mitigation systems/devices to improve the sustainability of active ranges regarding munition constituents (MC) release in the environment on different types of ranges, such as small arm range, impact area, grenade and demolition range. Sampling and observations were conducted during the trials to gain knowledge on efficiency and durability of designs. Some of the designs presented in this document were innovative, and others rely on existing technology. This presentation will focus on presenting the designs and the pros/cons that were noted on the different type of designs trialed in four ranges located in the province of Québec, Canada. Knowing that there are commercially available systems/designs on the market, the main goal of those trials is to further improve ranges in order to mitigate environmental impact with minimal infrastructure, technology and maintenance cost, all considering Canadian northern climate.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".