Environmental aspects of RIGHTRAC TDP green munitions
Bibliographic record
Abstract
The Defense Research and Development Canada (DRDC, Valcartier, QC) is developing new green explosive and propellant formulations, as part of a sustainable training strategy for the Canadian Army. The present research responds to the needs of DRDC by providing necessary physicochemical, chemical, and ecotoxicological data to help understand the environmental transport, fate and impact of new formulations developed within the RIGHTTRAC (Revolutionary Insensitive, Green and Healthier Training Technology with Reduced Adverse Contamination) project. The present study summarizes the dissolution, transport, transformation, and ecotoxicity of three propellant formulations, SP 7993, SP Unique, and CMR170, and their soluble components, NG, DPA, ATEC, MC, and EC. In addition, it gathers ecotoxicity data for an explosive formulation, GIM, which has been aged for periods varying from 6 to 24 months. Amongst the three propellant formulations tested, the single base formulation SP 7993 was found to be the most stable in terms of dissolution, even more stable than the formulation New Green M1 identified as the most stable of previously studied formulations. If scattered on soil surface and subjected to precipitations SP 7993 will give rise to low leakage of ATEC and the latter will not persist in soil. When comparing the two double base formulations, CRM170 appeared to be more stable than SP Unique. Although the MC/graphite coating present in CMR170 might have been responsible for the higher stability this could not be ascertained due to the concomitant higher NC content of CMR170, which also decreased its ability to dissolve.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".