MétaCan
Menu
Back to cohort
Record W2018356601 · doi:10.1063/1.4902836

A dynamic ball compression test for understanding rock crushing

2014· article· en· W2018356601 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueReview of Scientific Instruments · 2014
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsMaterials scienceBrittlenessDynamic loadingDynamic range compressionSplit-Hopkinson pressure barUltimate tensile strengthBall (mathematics)MechanicsDynamic testingRock mechanicsComposite materialGeotechnical engineeringStructural engineeringGeologyStrain ratePhysics

Abstract

fetched live from OpenAlex

During crushing, rock particles are subjected to complicated loading. It is desired to establish the relation between the loading and the fragmentation parameters for better understanding rock crushing mechanism. In this work, a split Hopkinson pressure bar system in combination with high speed cameras is utilized in the dynamic ball compression test, in which the spherical rock sample is adopted to avoid the shape effect. Using elasticity theory, the loading rate and the dynamic indirect tensile strength are first calculated. With the aid of the moment-trap technique and high speed cameras, the surface energy is determined for each sample. The relations between the loading rate and the fragmentation parameters, i.e., the number of fragments and the surface energy are established. The application of this method to a granitic rock shows that it is flexible and can be applied to the crushing study of generic brittle solids.

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.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.918
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.031
GPT teacher head0.271
Teacher spread0.240 · 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