Environmental Impacts of Training Activities at an Air Weapons Range
Bibliographic record
Abstract
Within Canada, it has been recognized in the last decade that military training activities may have impacts on the environmental quality of training ranges. However, impacts of activities specific to Air Force Bases have not yet been intensely documented. A hydrogeological study was accomplished at the Cold Lake Air Weapons Range, Alberta, to evaluate the environmental impacts of using bombs, rockets, strafing, and open burning/open detonation (OB/OD) on the quality of soil, ground water, surface water, and lake sediments. Samples were analyzed for metals, anions, ammonium perchlorate (NH(4)ClO(4)), and energetic materials (EM). It was found that training activities did not result in measured values being exceeded on the basis of guidance values for surface water and lake sediments. Contamination by metals was mostly limited to soils, and some metals may be related to the use of bombs (Cd, Cu, Pb), strafe (Cu), and rockets (As, Ba, Cd, Cr, Cu, Fe, Ni, Pb, U, V, Zn). TNT (2,4,6-trinitrotoluene) was the main EM found in soils, while RDX (hexahydro-1,3,5-trinitro-1,3,5-triazine) was more common in ground water. Both are related to live bombing, while nitroglycerine (NG) is related to rocket use and was detected in soils only. Aluminum, nitrate, and ammonium perchlorate detected in ground water may be related to live bombing or rockets. OB/OD operations resulted in the presence of various EM in soils, and of perchlorate and nitrate in ground water. Contamination by metals and explosives in soils was localized around the targets and varied significantly in time; however, in ground water it was more constant and may persist for a period of several years after a target has been removed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".