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Record W2920305069 · doi:10.1002/9783527816651.ch3

Assessment and Sustainment of the Environmental Health of Military Live‐fire Training Ranges

2019· other· en· W2920305069 on OpenAlexaffabout
Sonia Thiboutot, Sylvie Brochu

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsTraining (meteorology)PreparednessPopulationEnvironmental planningSustainabilityEngineeringAmmunitionVariety (cybernetics)Environmental resource managementRisk analysis (engineering)AeronauticsBusinessGeographyComputer scienceEnvironmental scienceEnvironmental healthEcologyPolitical scienceLawArtificial intelligenceMeteorology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.009
GPT teacher head0.248
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreOther

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".

Quick stats

Citations3
Published2019
Admission routes2
Has abstractyes

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