Evaluation of the Contamination by Explosives in Soils, Biomass and Surface Water at Cold Lake Air Weapons Range (CLAWR), Alberta, Phase 1 Report
Bibliographic record
Abstract
Abstract : This work describes the evaluation of the impacts of the live firing training activities in Cold Lake Air Weapons Range (CLAWR) in Alberta performed during August 02 (Phase I). CLAWR is the biggest air weapon range area in Canada and was the first Canadian Air Force Base to be characterized for explosives and metals. The study was conducted by DRDC-Valcartier in collaboration with the U.S. Army Engineer Research and Development Center (ERDC), Cold Regions Research Engineering Laboratory (CRREL), Hanover, NH, and the ERDC Environmental Laboratory (EL), Vicksburg, MS. The problem of Army ranges should be different from that of Air ranges since the Air weapons are different even if filled with the same explosives. Four ranges on the site were visited during August 2002. Alpha, Bravo, Jimmy Lake and Shaver Ranges were sampled for explosives using different strategies. More particularly, intensive efforts were done in the Shaver Range since this range was used mainly for air bombing. A linear transect sampling strategy was used in all ranges to evaluate the progression in explosives concentrations across the ranges. All the samples were built by compositing 20-30 sub-samples. A new circular sampling strategy adapted to the air-bombing situation was achieved by collecting 26 samples around the targets at specific locations. Some soil samples were also collected at different depths in front of the targets. In total, 193 soil samples, 13 biomass samples, and 4 surface water samples were collected during this first phase of the evaluation of this area. Metal analyses were done using Inductively Coupled Plasma /Mass spectrometer (ICP/MS) and explosives concentrations were done using the Gas Chromatography/Electron Capture Detector (GC/ECD) method developed at CRREL.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".