MétaCan
Menu
Back to cohort
Record W4403083037 · doi:10.1139/cgj-2023-0534

Contrast experiments on breaching characteristics of landslide dams and debris-flow dams

2024· article· en· W4403083037 on OpenAlexvenueno aff
Huayong Chen, Xiao Li, Hechun Ruan, Tao Wang

Bibliographic record

VenueCanadian Geotechnical Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsDebris flowGeotechnical engineeringLandslideGeologyDebrisContrast (vision)Computer science

Abstract

fetched live from OpenAlex

The difference in soil properties determines the different breaching characteristics exhibited by landslide dams (LDs) and debris-flow dams (DFDs). In this study, two types of soil were prepared by controlling the initial water content and the mixing time of the soil to construct the LD and DFD. Based on observations of breach in dams with six different grain size distributions, the following conclusions were drawn: (1) the erosion resistance within the soil leads to a slower failure speed for DFD under the same grain size distribution and particle density. However, both types of dams exhibit a nonuniform downcutting process in the longitudinal direction, induced by uneven velocities. (2) Laterally, DFDs are characterized by the creep slide of the breach bank, distinct from the intermittent slide observed in LD. (3) For the range of conditions tested, the peak discharge of LD significantly exceeds that of DFD. Additionally, the flood curve of LD exhibits a bimodal characteristic, attributed to the slide of the bank slope and the nonuniform distribution of particles within the dam. Finally, a prediction formula for the downcutting coefficient of the breach was established and validated by past studies. This study provides a basis for predicting outburst floods of LD and DFD.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.803
Threshold uncertainty score0.557

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.221
Teacher spread0.213 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicDam Engineering and SafetyFrench-language works237,207