MétaCan
Menu
Back to cohort
Record W4284709616 · doi:10.3389/feart.2022.916069

Kinematic Analysis of the 2020 Elliot Creek Landslide, British Columbia, Using Remote Sensing Data

2022· article· en· W4284709616 on OpenAlexafffundabout
Davide Donati, Doug Stead, Marten Geertsema, Jacob Bendle, Brian Menounos, Lisa Borgatti

Bibliographic record

VenueFrontiers in Earth Science · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsMinistry of ForestsUniversity of Northern British ColumbiaSimon Fraser University
FundersHakai InstituteNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsTula Foundation
KeywordsGeologyLandslideBedrockRockslideLineamentGeomorphologyGlacierLandslide classificationLidarGlacial periodRockfallSeismologyRemote sensingTectonics

Abstract

fetched live from OpenAlex

The 2020 Elliot Creek landslide-tsunami-flood cascade originated from an 18.3 Mm 3 rock slope failure in quartz diorite bedrock in a valley undergoing rapid glacial retreat. We used airborne LiDAR and optical imagery to characterize the slope and its surroundings. Using the LiDAR, we determined that two rockslides (2020 and an older undated one) occurred on this slope and shared a common basal rupture surface. We mapped two main sets of lineaments that represent structures that controlled the orientation of the lateral and rear release surfaces. Analysis of the topographic profile indicates a wedge-shaped failure block and a stepped rupture surface. Further topographic profile analysis indicates the possibility of a structurally controlled geomorphic step in the valley that corresponds with a change in the orientation of the valley. The rapid retreat of the West Grenville Glacier and the positions of the rupture surfaces suggest glacial retreat played a role in the landslides.

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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.006
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.220
Teacher spread0.209 · 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 designSimulation or modeling
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

Citations6
Published2022
Admission routes3
Has abstractyes

Explore more

Same venueFrontiers in Earth ScienceSame topicLandslides and related hazardsFrench-language works237,207