Erosion, deposition and contamination by high‐magnitude subaqueous debris flows and turbidity currents: Insights from the failure of a tailings dam near Quesnel Lake, British Columbia
Bibliographic record
Abstract
Abstract The 4 August 2014 failure of Mount Polley mine tailings dam in central British Columbia, Canada, is the worst environmental disaster in British Columbia history. Around 25 million m3 of water, tailings solids and dam construction materials travelled as a slurry flow down nearby Hazeltine Creek. About 18.6 million m3 of tailings and displaced coarse sediment entered into the narrow, deep, fjord‐like Quesnel Lake. Multibeam echosounding, sub‐bottom profiling, sediment cores and theoretical models are used to examine erosion and deposition on the Hazeltine Creek fan‐delta and the bed of Quesnel Lake by subaqueous sediment density flows generated from the slurry. Strong to very strong subaqueous debris flows eroded two deep subaqueous channels on the fan‐delta in a period of less than 12 h. A model for subaerial debris‐flow erosion overestimates submerged delta‐front erosion rates in the channels because of evolution of the subaqueous debris flows and incomplete liquefaction of the eroding bed. The debris flows deposited a localized debrite with a hummocky surface composed of fine tailings and coarse displaced fan‐delta and fluvial sediment. A flat, featureless, surface composed of fine‐grained sediment adjacent to the debrite is interpreted as debrite/turbidite resulting from debris flows proximally and turbidity currents distally. Copper concentrations in cores are highly elevated compared to background concentrations in pre‐event native lacustrine sediment. Deposits from subaqueous sediment flows generated by earthquakes and glacial lake outburst floods are generally thinner than those in Quesnel Lake caused by the tailings dam failure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".