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Record W2922185530 · doi:10.1680/jgeen.18.00207

Review and latest insights into rock fall temporal variability associated with weather

2019· article· en· W2922185530 on OpenAlexaffabout
Renato Macciotta

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Geotechnical Engineering · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHazardProbabilistic logicClimate changeFocus (optics)Data scienceMeteorologyComputer scienceClimatologyGeologyArtificial intelligenceGeography

Abstract

fetched live from OpenAlex

This paper presents a review of historical and latest insights into rock fall hazard temporal variability associated with weather. Reviewed research expands several locations around the world; however, focus has been on the recent advances in western Canada and from the author's experience. The recent research reviewed has provided new insights into the relationships between weather and rock fall occurrences and the recent focus on probabilistic approaches appears to be the way forward for rock fall hazard management. This paper also references some statistical tools for quantification of weather–rock fall relationships that allows better understanding of the stochastic nature of the phenomena. A decisive strength of how these methodologies are developing lies in the fact that adoption of probabilistic tools allows direct translation into rock fall hazard quantification that reflects its temporal variability. When coupled with weather forecasting, these tools can provide real-time forecasting of rock fall hazard. Moreover, some of these tools provide a way forward for forecasting the effects of climate change on rock fall hazards.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score0.446

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 head0.003
GPT teacher head0.173
Teacher spread0.169 · 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

Citations11
Published2019
Admission routes2
Has abstractyes

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