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Record W2582518569 · doi:10.1002/9781119321682.ch1

A Comparison of Damage in Glass and Ceramic Targets

2017· other· en· W2582518569 on OpenAlexfundno aff
Brady Aydelotte, Phillip Jannotti, Mark Andrews, Brian E. Schuster

Bibliographic record

VenueCeramic engineering and science proceedings · 2017
Typeother
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsnot available
FundersMcMaster University
KeywordsSPHERESMaterials scienceCurvaturePerpendicularPlane (geometry)CylinderComposite materialEnhanced Data Rates for GSM EvolutionCeramicCrackingCusp (singularity)Shot (pellet)GeometryMathematicsPhysicsMetallurgyEngineering

Abstract

fetched live from OpenAlex

This chapter examines how ballistic impacts on ceramics generate different types of damage including varying levels of comminution, cone cracking, and radial cracking through experiments on fused silica glass targets. The chapter shows how measurements of the fracture cone angle on a plane perpendicular to the plane containing the shot-line vector were consistently smaller for the same velocity. The experiments were conducted on glass cylinders. The cylinders were transparent on the ends and had a ground finish on the cylinder sides. The fused silica cylinders were impacted using either steel or borosilicate glass spheres. The steel spheres generally did so much damage to the cylinders that it was difficult to recover the samples for further analysis. Cone cracks which form as a result of oblique impacts have concave down curvature on the leading edge and concave up curvature on the trailing edge. The steel sphere data was omitted because steel sphere impacts produced cone cracks with much smaller included angles, probably as a result of the larger mass and the fact that the steel spheres remained intact during the impact 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)0.000
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.292
Teacher spread0.274 · 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
Published2017
Admission routes1
Has abstractyes

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