Canadian Programme on the Environmental Impacts of Munition.
Bibliographic record
Abstract
A large effort was dedicated in Canada over the last years on the characterization of military live fire training ranges for munitions residues and on the study of the environmental fate and ecotoxicological impacts of munitions constituents (MC). The patterns of contamination of each type of ranges, such as grenade or antitank ranges were identified and sampling guidance documents were published. The laboratory and large scale fate and transport of MC were assessed. Efforts were also made to define geological, hydrological and hydrogeological contexts of the major Army training ranges. Features such as the hydraulic head and the water quality, groundwater flow and direction were used to model the underlying aquifer. Attributes of each specific site were used to create vulnerability and hazard maps. The evaluation of the risk of aquifer contamination by military training activities was conducted by combining vulnerability and hazard maps. These maps will be used in Canada as range management tools to guide various decisions, such as new range location, current site relocation or range closure. All the information acquired over the last years allowed a deep understanding of the deposition, fate, toxicity and transport of MC. This paper will describe the Canadian approach towards understanding and minimizing the environmental footprint of munitions to support Forces readiness and the sustainable management of our ranges and training areas.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.003 |
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".