Grenade range managment using lime for dual role of metals immobilization and explosives transformation
Bibliographic record
Abstract
1. ABSTRACT: The importance of live fire training for US forces cannot be overestimated. The success of our armed forces depends upon realistic training utilizing the actual weapons and munitions that will be used in theatre during strategic and tactical operations. However, a drawback of this type of realistic training is the potential contamination of firing ranges. Most munitions-contaminated soils found on training ranges contain a mixture of compounds. For hand grenade ranges (HGR), the prevalent munition used is the fragmentation grenade, typically composed of an iron shell and Composition B explosive material. Studies performed on ranges in both the United States and Canada have shown that there is a large degree of variability in munitions constituent type, concentration, size, and spatial distribution occurring on single ranges, as well as between different ranges. Contaminants present include explosive residue in the form of Composition B and Trinitrotolene as well as metals such as zinc, iron, manganese, calcium, lead, chromium, copper, nickel, molybdenum, vanadium, and tin. The optimum treatment technology for successful remediation of distributed energetics and metals at training ranges should be inexpensive, easily applied in remote locations, effective on heterogeneous contaminant distributions, effective over large areas, effective on multiple compounds, be non-intrusive, to the extent possible, and able to be incorporated into normal range maintenance operations. Technologies currently available for the remediation of munitions-contaminated soil and groundwater do not meet these criteria. The objectives of this study were to evaluate and develop a cost
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.001 |
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.
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".