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Anisotropy in fracture toughness of shale and coal under dynamic loading

2020· preprint· en· W3065582798 on OpenAlexaff
Xiaoshan Shi, Yixin Zhao, Shuang Gong, Wei Wang, Wei Yao

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicGeotechnical and Geomechanical Engineering
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBeddingOil shaleCoalFracture toughnessMaterials scienceBedAnisotropyComposite materialToughnessGeologyChemistry

Abstract

fetched live from OpenAlex

Notched semi-circular bend (NSCB) samples are prepared with different bedding angles and subjected to dynamic loading by a modified split Hopkinson pressure bar (SHPB) system. The static fracture toughness (SFT) of shale or coal increases linearly with the bedding angle. Under similar loading rates, the dynamic fracture toughness (DFT) of shale increases as the bedding angle rises. However, the DFT of coal is much discrete. The DFT of shale or coal increases with loading rate increasing. DFT is much higher than SFT. For shale, there is almost a linearly positive correlation between DFT and loading rate, while for coal, there is a logarithmic relationship. All values of coal are much smaller than that of shale. As the loading rate increases, the effect of bedding angle on DFT attennuates. Notably, for bedding angle of 45°, the cracking mode of coal is more easily affected by bedding plane, than for other angles.

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: none
Teacher disagreement score0.915
Threshold uncertainty score0.985

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 integrity0.0000.001
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 head0.009
GPT teacher head0.215
Teacher spread0.206 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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