Some constraints on the severity of landslide penetration in sensitive deposits
Bibliographic record
Abstract
Theoretical considerations and case-studies are presented to show that, in many cases, the severity of retrogression of an unstable valley slope, in areas of sensitive muddy sediment, is controlled by the topography of the valley. A formula is offered to predict the distance of retrogression from topographic attributes of valleys. It is suggested that most retrogressive landslides in sensitive sediments involve only limited liquefaction of the spoil, and it is for this reason that retrogression is controlled by this topographic constraint. Those situations in which retrogression stops before this limit is reached are also discussed: one important factor which can determine whether or not such aborted retrogression will occur appears to be the nature of the first-time slide. Those situations in which retrogression can exceed this topographic limit are briefly examined as well: attention is focussed on the importance of spoil liquefaction as a prerequisite for such excess landslide retrogression. Data are presented which indicate that the initial undrained strength of the sediment exerts a major control on the degree of spoil liquefaction. Finally the possibility is considered that some assumed retrogressive failures were in fact flake slides in which the slide mass disintegrated after failure. Such 'retrogressive facsimiles' are considered to be rare.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".