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Record W2514584825 · doi:10.15273/ijge.2016.03.013

Geophysical Investigation and Management Plan of a Shallow Landslide along the NH-44 in Atharamura Hill, Tripura, India

2016· article· en· W2514584825 on OpenAlexvenueno aff
Kapil Ghosh, Shreya Bandyopadhyay, Sunil Kumar De

Bibliographic record

VenueInternational journal of geohazards and environment · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsnot available
Fundersnot available
KeywordsLandslideGeologySiltFault scarpTerrainGeophysical surveyBoreholeMonsoonHydrology (agriculture)Geotechnical engineeringGeomorphologyMining engineeringGeophysicsSeismologyTectonicsOceanographyCartography

Abstract

fetched live from OpenAlex

In any effective landslide hazard mitigation plan, in-depth knowledge about the causes of instability is required. Consequently, it is essential to study geological, geotechnical, meteorological and anthropogenic factors that influence the landslide. For the present study a geophysical investigation has been carried out on a large landslide, on the slopes of Tertiary siltstones, mudstones, shales and sandstones at Atharamura Hill, Tripura. Different techniques have been used, including measurement of micro-slope using Total Station, clinometers and the LISCAD software for terrain modeling and determining the total volume of the material displaced and total area affected by landslides and analysis of soil properties to understand the present condition of the scarps. Results obtained through field investigation and laboratory testing revealed that the underlying cause of the slide could be (a) the adverse geological formation with unconsolidated sandy materials and occasional intersection of silt or clay layers, (b) the hydrological condition with continuous seepage through fractures, and (c) cutting of hill slopes for reconstructing and widening of the road (NH44). This latter anthropogenic influence has been triggered by an intense precipitation event during the monsoon season.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.203
Threshold uncertainty score0.253

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 integrity0.0000.000
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 head0.007
GPT teacher head0.199
Teacher spread0.192 · 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 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

Citations3
Published2016
Admission routes1
Has abstractyes

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