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Record W2767538683 · doi:10.1520/jte20160244

Development and Application of the New Explosive Loading Experimental System of Digital Laser Dynamic Caustics

2017· article· en· W2767538683 on OpenAlexaff
Yi-Ning Wang

Bibliographic record

VenueJournal of Testing and Evaluation · 2017
Typearticle
Languageen
FieldEngineering
TopicOptical Systems and Laser Technology
Canadian institutionsGeomechanica (Canada)
Fundersnot available
KeywordsHigh-speed photographyExplosive materialHigh-speed cameraLaserDigital cameraOpticsPhotographyFrame (networking)BoreholeSPARK (programming language)Computer scienceAcousticsMaterials scienceEngineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Abstract The new explosive loading experimental system of digital laser dynamic caustics was developed by replacing the conventional multi-spark point source light and the previous multi-frame film camera with the solid-state laser and the digital high-speed camera, respectively; namely, a new caustics experimental system of high-speed photography system using a combination of solid-state laser and digital high-speed camera was created. This new experimental system was used to conduct the crack propagation test of single-borehole explosion with defective medium and the double-boreholes explosive loading experiment to obtain the clear digital caustics photos. If compared with the conventional optical photos, these digital photos were found to be clearer with more reliable results and longer recording time, which demonstrates that the new system can meet the requirements of ultra-dynamic fracture problems such as blasting. The new optical system is simple, user-friendly, which ensures a low experimental cost, short cycle, and the continuous observation throughout the fracture process.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.031
GPT teacher head0.279
Teacher spread0.247 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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