The sedimentological and Arsenic contamination history of Frame Lake, Yellowknife, Northwest Territories, Canada
Bibliographic record
Abstract
Frame Lake, a small (88.4 ha), shallow (max < 6.5m), high-latitude lake found within the city limits of Yellowknife, Northwest Territories, Canada was selected due to the known legacy contamination of the lake's sediments to test the feasibility of using seismic subbottom profiling to estimate total volumes of heavy metal contaminated sediments in lacustrine environments.To ground-truth the sub-bottom profiling results, physical and ICP-MS analyses were carried out on freeze cores collected from Frame Lake's southern basin, and sedimentological marker beds and 14C dating was used to chronologically constrain the lake depositional history.ICP-MS results showed high levels of arsenic contamination (up to 1538 µg g-1) in late 20th-century lake sediments, which contrasts sharply with measured Holocene values that averaged only 16 µg g-1 (n=41, ± 5.4 SD).The high arsenic content in lakebed sediments, which tends to be concentrated within specific horizons, results in distinct seismic reflectors within the acquired Sonar data.Stratigraphic horizons where arsenic was concentrated do not necessarily correlate with actual depositional events as changes in lake hydrology and redox conditions have resulted in remobilization and migration of arsenic in lake sediments.Direct GIS software comparison of core data against the sub-bottom profiler transect results permitted an interpolated lateral and vertical reconstruction of the distribution of variously contaminated sediments throughout the entire lake basin.Based on our analysis, a minimum of ~230,000 m3 of contaminated sediments would need to be dredged from Frame Lake to achieve a minimum residual sediment arsenic concentration of < 150 µg g-1.
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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 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".