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Record W4414402763 · doi:10.1029/2025gl117126

Interplay of Loading and Adsorption Controls Elastic Deformation of Clastic and Crystalline Rocks

2025· article· en· W4414402763 on OpenAlexaff
Rui Wu, Hongpu Kang, Fuqiang Gao, Bing Q. Li, Kerry Leith, Qinghua Lei, Gennady Y. Gor, Paul Antony Selvadurai, Xiangyuan Peng, Shuangyong Dong, Y. Li

Bibliographic record

VenueGeophysical Research Letters · 2025
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsWestern University
FundersMinistry of Business, Innovation and EmploymentNational Natural Science Foundation of ChinaChina Coal Technology Engineering Group
KeywordsSofteningStiffeningStiffnessMoistureElasticity (physics)AdsorptionOverburdenElastic modulus

Abstract

fetched live from OpenAlex

Abstract Rock elasticity varies with both mechanical loading and moisture content. Studies to date have only examined each effect independently, although moisture interactions with pore walls are likely coupled to mechanical stress. Here, we present experimental data specifically collected in sandstone and granite under simultaneous control of cyclic loading alongside ambient humidity approaching saturated vapor. Adsorption can account for 40% reduction in Young's modulus, which reduces to 10% as uniaxial stress increases from below 1 MPa to below the elastic limit. The observation is explained by a micromechanical model linking grain‐scale contact stiffness to pore‐scale vapor adsorption, quantitatively capturing coupled stress‐induced stiffening and adsorption‐induced softening. The coupled behavior is interpreted as adsorption‐induced softening becoming inhibited under greater mechanical loads. Our results suggest the coupled effects are strongest at overburden stresses between 3.3 and 10.6 MPa (140–450 m) in sandstone and 6–30.3 MPa (235–1,200 m) in granite.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 head0.272
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2025
Admission routes1
Has abstractyes

Explore more

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