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Record W4297516183 · doi:10.3390/geosciences12100359

Updated Understanding of the Thompson River Valley Landslides Kinematics Using Satellite InSAR

2022· article· en· W4297516183 on OpenAlexaffabout
Amir Soltanieh, Renato Macciotta

Bibliographic record

VenueGeosciences · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLandslideGeologyInterferometric synthetic aperture radarFault scarpKinematicsLandslide classificationGeodesySatelliteGeomorphologySeismologyRemote sensingSynthetic aperture radarTectonics

Abstract

fetched live from OpenAlex

The Thompson River valley is one of the most important transportation corridors in western Canada as it hosts two important railways. This valley has experienced several historical landslide events, many of them along a 10 km section south of the town of Ashcroft. Six of these landslides, showing varying states of activity, were selected for analysis in this paper, as these have the potential for the biggest impact on the railways. The subsurface interpretation of these landslides is combined with satellite InSAR data from May 2015 to May 2017 to enhance the current understanding of the landslide kinematics. Two InSAR orientations are combined geometrically with the assumption that the horizontal component of landslide movement is parallel to the slope azimuth, which provides a practicable approach to approximate landslide displacement vectors. The results classify these landslides as very slow-moving. The maximum velocities recorded are 29, 35, 26, 64, 18, and 52 mm/year for the Goddard, North, South, South extension, Barnard, and Redhill landslides, respectively. All landslides except the Redhill landslide show near-horizontal movements near the toe, with increasing vertical components as measurements approach the back scarp. This confirms that kinematics include rotational and compound mechanisms.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.032
GPT teacher head0.235
Teacher spread0.203 · 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.

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

Citations4
Published2022
Admission routes2
Has abstractyes

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