Bibliographic record
Abstract
Greener munitions are designed to exhibit less environmental and health impacts without adversely affecting their current performance or their insensitivity levels. The most obvious way of limiting the environmental impact consists in changing the current energetic ingredients for others, less noxious. However, the incorporation of a new ingredient into a munition is a very complex process that is definitely not as trivial as it may seem. Very few candidates are fielded. Even then, unforeseen events sometimes occur after the munitions are fielded, often with a new weapon system. Given the actual lack of the perfect replacement candidate, greener munitions are looked at from another perspective, based on the risk to sensitive receptors rather than on the search for zero environmental impact. The notion of ‘green’ is approached by evaluating the bioavailability of each potential replacement ingredient as well as the eventual exposure of sensitive receptors. In addition, a holistic approach was taken to evaluate the potential of developing greener munitions, while including other critical parameters. This integrated approach, viewed as a model for developing greener munitions, successfully demonstrated, with two detailed examples on a greener artillery munition and an RDX-free plastic explosive, that the evaluation of the environmental impacts at the beginning of a munition development cycle simultaneously with the performance and IM properties avoid dedicating considerable efforts on the development of formulations that could be discarded at the end of the development cycle due to noxious environmental impacts.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".