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Record W3022916852 · doi:10.1029/2020gl087505

Decreasing Landslide Erosion on Steeper Slopes in Soil‐Mantled Landscapes

2020· article· en· W3022916852 on OpenAlexaff
J. Prancevic, Michael P. Lamb, Brian W. McArdell, Christian Rickli, James W. Kirchner

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsConcordia University
FundersETH Zürich FoundationUniscientia StiftungSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsLandslideGeologyCohesion (chemistry)ErosionSoil waterGeomorphologyLandslide mitigationCreepGeotechnical engineeringLandslide classificationSoil science

Abstract

fetched live from OpenAlex

Abstract Slope‐stability models predict that steeper hillslopes require smaller hydrological triggers for shallow landslides to occur due to the added downslope pull of gravity, which should result in more frequent landslides and faster erosion. However, field observations indicate that landslide frequency does not consistently increase on steeper hillslopes. Here, we use measurements of 1,096 soil landslides in California and Switzerland, and a compilation of landslide geometries, to show that steeper hillslopes typically have thinner soils and that thin soils inhibit landslides due to enhanced roles of cohesion and boundary stresses. We find that the landscape‐averaged landslide erosion depth peaks near the threshold slope for instability, and it drops to half that value on hillslopes that are just 5° to 10° steeper. We propose that faster rates of soil creep on steeper slopes cause thin and more stable soils, which in turn reduces landslide erosion, despite the added pull of gravity.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.750
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.003

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 head0.027
GPT teacher head0.281
Teacher spread0.254 · 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

Citations83
Published2020
Admission routes1
Has abstractyes

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