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Record W3202391504 · doi:10.1144/m58-2021-1

Rock properties and rock-controlled landforms

2021· article· en· W3202391504 on OpenAlexaff
Filip Duszyński, Derek Ford, Andrew Goudie, Piotr Migoń

Bibliographic record

VenueGeological Society London Memoirs · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLandformSchmidt hammerGeologyPermeability (electromagnetism)Rock mass classificationPorosityGeomorphologyThin sectionGeochemistryGeotechnical engineeringCompressive strength

Abstract

fetched live from OpenAlex

Abstract Rock properties are a crucial control of landform development. The purpose of this chapter is to examine the progress that was made in studying rock properties in general and then to discuss developments in the study of landforms in three main rock types: granite, limestone and sandstone. From the mid-1960s onwards, geomorphology witnessed an increasing concern with the quantification of rock properties and their relationship to landforms and landscape evolution. Japanese geomorphologists led in this endeavour. Studies crossed a range of scales from those of a large size that were susceptible to field measurements to those of small size that involved laboratory studies. Among the basic characteristics of rocks that have been studied are fracturing and jointing, rock mass strength, hardness as determined by the Schmidt Hammer, resistance as determined by laboratory simulations, slaking susceptibility, porosity, water absorption capacity, water content and permeability, and petrological thin-section analyses. The investigation of forms and processes in granite, limestone and sandstone areas has shown the value of combined geological and geographical approaches, and the increasing internationalization of studies.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.527
Threshold uncertainty score0.996

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)0.0050.001

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 head0.012
GPT teacher head0.195
Teacher spread0.183 · 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

Citations11
Published2021
Admission routes1
Has abstractyes

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