Gun Propellant Residues Dispersed from Static Artillery Firings of LG1 Mark 2 and C3 105-mm Howitzers
Bibliographic record
Abstract
Abstract : Military training on fields and ranges at Canadian Forces Bases (CFB) is essential to prepare our troops for potential wars and/or peace missions. On the other hand, the growing concern of DND leaders and of the general population makes it necessary to evaluate the impacts of training on the environment. During the last 10 years, new methods of characterization have been developed to assess the energetic materials contamination, which is different from the usual contamination in residential or industrial scenarios. Recently, the efforts were focused on firing positions. Soil and biomass sampled at firing positions have shown detectable levels of gun propellant residues, such as 2,4-dinitrotoluene (2,4-DNT) and nitroglycerine (NG). In this study, aluminium witness plates were placed in front of the muzzle of the gun to collect residues propelled in the environment. Cotton wipes were used to collect the residues on plates. Moreover, as complementary data, soil samples were taken before and after the military exercise using a composite approach to be statistically representative. The energetic materials were analyzed at DRDC Valcartier in Quebec City by high performance liquid chromatography (HPLC) and metal analyses were performed at Bodycote Testing Group in Montreal only for soil samples. This work was realized in May 2005 and was supported by the Sustain Thrust of DRDC and the Strategic Environmental Research and Development Program (SERDP), Washington D.C., USA..
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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 | 0.000 |
| 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) | 0.002 | 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".