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Record W2759696502 · doi:10.1139/cgj-2017-0085

The Saint-Jude landslide of 10 May 2010, Quebec, Canada: Investigation and characterization of the landslide and its failure mechanism

2017· article· en· W2759696502 on OpenAlexaffvenueabout
Ariane Locat, Pascal Locat, Denis Demers, Serge Leroueil, Denis Robitaille, Guy Lefebvre

Bibliographic record

VenueCanadian Geotechnical Journal · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsUniversité de SherbrookeMinistère des TransportsUniversité Laval
Fundersnot available
KeywordsLandslideGeologyPiezometerGeotechnical engineeringDebrisFailure mechanismGeotechnical investigationFault scarpSiltGeomorphologyBedrockPenetrometerCone penetration testSeismologyAquiferGroundwaterFault (geology)Soil waterEngineering

Abstract

fetched live from OpenAlex

A landslide occurred on 10 May 2010, along the Salvail River, in the municipality of Saint-Jude, Quebec. Debris of the landslide was formed of clay having horst and graben shapes, typical of spreads in sensitive clays. A detailed investigation was carried out by the Ministère des Transports, de la Mobilité durable et de l’électrification des transports du Québec, in collaboration with Université Laval, with the objective of characterizing this landslide, determining the causes, and learning about its failure mechanism. The soil involved was a firm, grey, sensitive, lightly overconsolidated clay with some silt. Data from piezometers installed near the landslide indicated artesian conditions underneath the Salvail River. Cone penetration tests allowed the location of two failure surface levels: the first one starting 2.5 m below the initial river bed, extending horizontally up to 125 m, and a second one 10 m higher, reaching the backscarp. Investigation of the debris with onsite measurements, light detector and ranging surveys, cone penetration tests, and boreholes allowed a detailed geotechnical and morphological analysis of the debris and reconstitution of the dislocation mechanism of this complex spread.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.551
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.181
Teacher spread0.174 · 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 teacher head, 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

Citations60
Published2017
Admission routes3
Has abstractyes

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