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Record W2277404748 · doi:10.1139/cgj-2015-0344

Field hydrological monitoring of a sloping shallow pyroclastic deposit

2016· article· en· W2277404748 on OpenAlexvenueno aff
Luca Comegna, Emilia Damiano, Roberto Greco, Andrea Guida, Lucio Olivares, Luciano Picarelli

Bibliographic record

VenueCanadian Geotechnical Journal · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsnot available
Fundersnot available
KeywordsLandslideGeologyHydrology (agriculture)Pyroclastic rockForcing (mathematics)VolcanoDebrisWater contentSuctionCurrent (fluid)GeomorphologyGeotechnical engineeringClimatologyMeteorologySeismologyOceanography

Abstract

fetched live from OpenAlex

Many mountainous areas in Campania, southern Italy, are characterized by steep slopes covered by unsaturated volcanic deposits. Shallow landslides are frequently triggered by intense and persistent rainfall events, often turning into debris flows that cause huge damage and casualties. Field hydrological monitoring is a useful tool to develop consistent models of slope response to rainfall, in terms of soil suction and moisture, and to define landslide triggering conditions. This is one of the reasons why since 2002 field monitoring is being carried out in Cervinara, around 50 km northeast of Naples. Since October 2009, rainfall height, soil suction and water content at several locations and depths along the slope are automatically being monitored. The data collected help to demonstrate the effectiveness of such a system for better understanding the hydrological processes occurring in similar slopes of Campania, allowing to distinguish between seasonal suction fluctuations, related to long-term meteorological forcing, and short-term response to rainstorms.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score0.999

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)0.0020.000

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 head0.208
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

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

Citations88
Published2016
Admission routes1
Has abstractyes

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