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Record W4322011670 · doi:10.5194/egusphere-egu23-13454

Quick-clay landslides; stability of soil influenced by sedimentological and hydrogeological factors

2023· preprint· en· W4322011670 on OpenAlexaboutno aff
Lene Pallesen, Ola Fredin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsnot available
Fundersnot available
KeywordsLandslideGeologyHydrogeologyGlacial periodFluvialErosionGroundwaterLeaching (pedology)GeochemistryGeomorphologyHydrology (agriculture)Geotechnical engineeringSoil scienceSoil waterStructural basin

Abstract

fetched live from OpenAlex

Quick-clay landslides can occur suddenly in marine and glaciomarine deposits below the marine limit (the highest sea-level after the last glaciation), sometimes with catastrophic consequences. Through geological time, the most common trigger for quick-clay landslides is fluvial erosion, undercutting the unstable slope. However, in more recent years human activity has been shown to be an increasingly important triggering cause. The main problem in quick-clay hazard management is the difficulty in predicting where and when a quick-clay landslide will occur. The deposition of marine clay and later isostatic uplift has exposed clay to leaching by fresh water, and formation of sensitive clay in previous glacial regions including Fennoscandia and Canada. The speed and limit of these processes depends on a variety of factors, including the presence of coarse-grained layers interbedded with clay, allowing groundwater to percolate into the deposit and leaching of salts that originally stabilize the clay. This PhD project is in its starting phase and aims to further the understanding of the complexity of marine clay deposits. This includes statistical analysis of landslides in marine deposits, examining sedimentological architecture, and hydrogeological modelling. Examining multiple locations in Norway will allow for comparison of landslide occurrence in relation to morphology, stratigraphy, and erosion. This can aid in improving the hazard management in these areas, to consider if and where landslides are more likely to occur, and at which scale.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.023
GPT teacher head0.247
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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