What are the controls for delta front slope failure? Insights from detailed monitoring at Squamish Delta, British Columbia.
Bibliographic record
Abstract
Submarine slope failures pose a hazard to seafloor infrastructure and coastal communities. Given the high population densities, slope failures can have a particularly significant impact around river deltas, generating damaging tsunamis and breaking critical telecommunications connections. Despite the risks they pose, a lack of detailed monitoring means that the factors that lead to slope collapse remain poorly constrained. Numerical modelling is typically used to assess future slope stability. Still, sparse existing data ensure that we cannot yet determine how submerged delta slopes evolve and progress to failure at the field scale. Here, we aim to close this gap by analysing repeat seafloor surveys of the submerged Squamish prodelta, British Columbia, to determine the physical controls on slope instability. Multibeam bathymetric surveys were performed on 93 consecutive weekdays in 2011, during which time at least five large (>50,000 m3) delta slope collapses occurred, as well as numerous smaller slope failures. These surveys allow us to determine how the delta slope and geometry changes on an unusually detailed timeframe (i.e. daily) in the build-up to slope collapse and how it relates to variable sediment supply from the feeding river and tidal fluctuations. Analysis of the five large collapses reveals that a single mechanism is not responsible for every failure. So, we investigated how different parts of the delta encounter major failure at different times and locations by measuring and mapping out the delta head and associating it with sediment input and tide high. From this, we found that slope failure is likely due to a combination of enhanced slope geometry due to delta lip progradation and pore pressure fluctuations relating to sediment loading and tidal effects.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".