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Record W6906563469 · doi:10.17632/zfnf4kpbhb.1

DATASET FOR: High-Quality Freeform Machining of Chemically Strengthened Glass Using Spark Assisted Chemical Engraving

2025· dataset· en· W6906563469 on OpenAlexaff

Bibliographic record

VenueMendeley Data · 2025
Typedataset
Languageen
Field
Topic
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsMachiningEngravingSPARK (programming language)Electrical discharge machiningQuality (philosophy)Raw material

Abstract

fetched live from OpenAlex

This dataset contains the raw experimental data and microscopic images supporting the findings of the manuscript "High-Quality Freeform Machining of Chemically Strengthened Glass Using Spark Assisted Chemical Engraving." The research investigates the application of Spark Assisted Chemical Engraving (SACE) for the high-quality freeform cutting of Chemically Strengthened Glass (CSG), specifically Corning® Gorilla Glass 3. The data was generated using a custom Response Surface Methodology (RSM) to systematically evaluate the effects of key SACE parameters (Voltage, Cutting Speed, Electrolyte Concentration) on final cut quality. The dataset includes the complete experimental design (32 runs) and the corresponding measured responses used for statistical analysis and model generation in the manuscript. The raw microscopic images provide visual evidence of the cut quality under various machining conditions.

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)0.005
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science, Research integrity
Consensus categoriesMeta-epidemiology (narrow), Open science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0100.010
Research integrity0.0010.002
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 head0.136
GPT teacher head0.403
Teacher spread0.267 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2025
Admission routes1
Has abstractyes

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