Assessment and Sustainment of the Environmental Health of Military Live‐fire Training Ranges
Notice bibliographique
Résumé
Ensuring preparedness of military troops involves operations and training with weapons systems over vast areas of land. Military forces have a responsibility to show leadership in environmental sustainability of their actions and to manage assets efficiently. A deep understanding of munitions' environmental footprints allows preventive or corrective actions, as well as sustaining our priceless training assets. The closure of training ranges due to uncontrolled adverse environmental impact would represent a tremendous loss to any country, as it would be almost impossible to open any new ranges thanks to population growth and the related encroachment. This chapter covers the precise and efficient measurement of munitions' environmental footprints, in order to assess the environmental health of military live-fire training ranges. This first step is the key to performing a strong and efficient risk management approach, by identifying and quantifying the risks with accuracy and precision. This represents a huge challenge when assessing large tracts of land in order to monitor the presence of munition residues in a multitude of training scenarios using a wide variety of weapons systems. As an example, the Canadian Department of National Defence manages more than 2 million hectares of land, with a large portion being dedicated to live-fire training. Not that long ago, it was believed that the use of munitions would only leave forensic traces of residues in the environment. This paradigm was proved false, and it was demonstrated that the munition residues that accumulated either at the firing position (FP) or at the target impact areas were enough to raise levels of concern. There was no protocol to address this issue and one had to be developed, to obtain representative results in a multitude of live-fire training scenarios. All the steps, from sampling process to sample treatment and analysis, had to be developed and validated. Reference values for munition constituents also had to be established and their fate and transport studied to obtain a clear understanding of the associated risks. To better define the specific sources of munition residues, protocols that contribute to the intimate knowledge of combustion processes and detonation efficiency were developed. By a deep understanding of munitions' footprints, tailored solutions can be developed to minimize or eliminate adverse impacts, and a few case studies are described. The new challenges ahead with novel munition constituents are also covered to avoid repeating the mistakes from the past. Finally, the numerous challenges, pitfalls, and successes in the journey towards sustainable ranges are described.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».