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Record W2782330464 · doi:10.1186/s40677-017-0090-0

Longevity of dams from landslides with sub-channel rupture surfaces, Peace River region, Canada

2018· article· en· W2782330464 on OpenAlexafffundabout
Brendan Miller, Anja Dufresne, Marten Geertsema, Nigel Atkinson, Heidi Evensen, D. M. Crudën

Bibliographic record

VenueGeoenvironmental Disasters · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsUniversity of AlbertaMinistry of Transportation of OntarioMinistry of Forests
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Alberta
KeywordsLandslideGeologyHydrology (agriculture)SedimentGlacial periodDrainage basinChannel (broadcasting)Structural basinGeomorphologySlope stabilityGlacial lakePhysical geographyGeotechnical engineeringGeography

Abstract

fetched live from OpenAlex

We examined seven landslide dams and their changes over time in the Peace River region of Canada. These landslides had subchannel rupture surfaces in glacial and glaciolacustrine sediments. We assessed the stability of the dams using 6 separate, morphometric-based stability indices (with a total of 10 stability thresholds). The landslides caused the streambeds to be elevated from 4 to 30 m forming the dams. The landslide lakes diminished in size over one to several years through stream incision into the dams and sediment infilling. The longest-lived dam persisted for up to 20 years. For two dams, incision into the dams lowered the lake levels by about half of the total depth, while the remainder of the water in the basins was displaced by sediment infilling. After the lakes drain, the sediment accumulations behind the dams can persist for decades. The stability analyses overpredicted unstable conditions which is inconsistent with the observed longterm persistence of the dams. The landslide dams in our study were relatively stable. Their lakes persisted for up to 2 decades and diminished over time through a combination of slow incision and basin infilling. The stability indices we assessed overpredicted unstable conditions and thus would require modification for these particular types of dams in this regional setting.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.005
GPT teacher head0.171
Teacher spread0.166 · 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 source (direct Gemma or distilled Codex), 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

Citations22
Published2018
Admission routes3
Has abstractyes

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