Tsunami modelling of the 7250 cal years BP Betsiamites submarine landslide
Bibliographic record
Abstract
Abstract On the north shore of the St Lawrence Estuary (Québec, Canada), near the Betsiamites river delta, a large sub-aerial submarine landslide complex was mapped using multi-beam bathymetry and light detection and ranging (LiDAR) data. Previous analysis of this landslide complex revealed that, since 7250 cal years BP, at least four different landsliding events occurred to form the present morphology, in which over 2 km 3 of material have been mobilized. The 7.25 cal ka BP landslide is of particular interest here: this landslide is entirely submarine and mobilized about 1.3 km 3 of material, deposited over an area of 54 km 2 , which make this landslide the largest identified on the St Lawrence estuary seafloor. This landslide showed a runout distance of about 15 km. Landslide-generated tsunamis may be triggered by such a landslide, where a large volume of material is mobilized in a short time. Kinematic analysis of this landslide was previously performed, and here we use these analyses in order to perform tsunami wave generation and propagation modelling. It is shown in this analysis that, even if the mobilized volume is very high and there is a long runout, the tsunami generated is small with tsunami wave amplitudes of <1.5 m, except in the vicinity of the landslide. The highest tide elevation in this part of the St Lawrence Estuary is about 5.5 m, so the impact of such a tsunami wave may be limited.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".