Sediment Trend Analysis in Support of Unexploded Explosive Ordnance Risk Assessment
Bibliographic record
Abstract
Abstract For 48 years (1952–2000) the south half of Lac Saint-Pierre, a widening of the St. Lawrence River between Montreal and Quebec, was used as an experimental firing range. Some 500,000 shells were fired into the lake, of which about 8000 are thought to contain energetic material. Approximately 2000 projectiles are considered as unexploded explosive ordnance (UXO). Furthermore, the lake itself is potentially the recipient of other contaminants associated with industrial wastes and agricultural runoff. The Canadian Department of National Defence, in order to research appropriate clearance approaches programs, instigated a large sediment transport study (sediment trend analysis or STA) to determine both sediment and contaminant sources and sinks and to assess the possible environmental consequences of various clearance options. STA is an empirical technique whereby patterns of sediment transport and dynamic behavior are derived from relative changes in grain-size distributions. About 1000 samples were collected at 500 m spacing encompassing the entire firing range. The lake bottom contained a complex number of sediment types ranging from “ancient” late glacial deposits, recent mud and sand, and a variety of mixtures. The STA identified the significance of the late glacial deposits as the dominant source for the sediments contained in the lake. The latter showed complex patterns of transport that originated from the ancient sediments and terminated on well-sorted sand. The findings demonstrated that anthropogenic contaminants were unlikely to be deposited in the lake sediments, or if they were, self-cleaning by natural processes would likely be rapid. Furthermore, trace metals were strongly associated with the ancient sediments evidently originating from an underlying anaerobic black shale formation. The STA provides an important framework to understand the chemistry of the sediments and their dynamics and helps to define the context within which the UXO clearance and environmental remediation options can be developed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.001 | 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".