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Record W1860944057 · doi:10.1002/esp.3493

Terrestrial laser scanning of rock slope instabilities

2013· article· en· W1860944057 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEarth Surface Processes and Landforms · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicRemote Sensing and LiDAR Applications
Canadian institutionsEmergent BioSolutions (Canada)
Fundersnot available
KeywordsRockfallGeologyPoint cloudTerrainClassification of discontinuitiesLaser scanningDeformation monitoringKey (lock)Remote sensingLandslideDeformation (meteorology)Mining engineeringComputer scienceGeotechnical engineeringArtificial intelligenceLaserCartographyGeography

Abstract

fetched live from OpenAlex

ABSTRACT This manuscript presents a review on the application of a remote sensing technique (terrestrial laser scanning, TLS) to a well‐known topic (rock slope characterization and monitoring). Although the number of publications on the use of TLS in rock slope studies has rapidly increased in the last 5–10 years, little effort has been made to review the key developments, establish a code of best practice and unify future research approaches. The acquisition of dense 3D terrain information with high accuracy, high data acquisition speed and increasingly efficient post‐processing workflows is helping to better quantify key parameters of rock slope instabilities across spatial and temporal scales ranging from cubic decimetres to millions of cubic metres and from hours to years, respectively. Key insights into the use of TLS in rock slope investigations include: (a) the capability of remotely obtaining the orientation of slope discontinuities, which constitutes a great step forward in rock mechanics; (b) the possibility to monitor rock slopes which allows not only the accurate quantification of rockfall rates across wide areas but also the spatio‐temporal modelling of rock slope deformation with an unprecedented level of detail. Studying rock slopes using TLS presents a series of key challenges, from accounting for the fractal character of rock surface to detecting the precursory deformation that may help in the future prediction of rock failures. Further investigation on the development of new algorithms for point cloud filtering, segmentation, feature extraction, deformation tracking and change detection will significantly improve our understanding on how rock slopes behave and evolve. Perspectives include the use of new 3D sensing devices and the adaptation of techniques and methods recently developed in other disciplines as robotics and 3D computer‐vision to rock slope instabilities research. Copyright © 2013 John Wiley & Sons, Ltd.

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.

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 score0.131
Threshold uncertainty score0.618

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)0.0010.000

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 head0.009
GPT teacher head0.207
Teacher spread0.198 · 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