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Record W4413909755 · doi:10.1201/9781003710141-18

The Art of Waterjet Cutting

2025· book-chapter· en· W4413909755 on OpenAlexaff
Peter H.-T. Liu

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicErosion and Abrasive Machining
Canadian institutionsPQ Corporation (Canada)
Fundersnot available
KeywordsGeology

Abstract

fetched live from OpenAlex

This chapter explores the integration of waterjet technology into UNC-Asheville’s STEAM Studio, a collaborative maker space for engineering and art students. The studio’s expansion in 2016 included an OMAX Maxiem® 1530 abrasive waterjet, which has significantly enhanced both educational and creative projects. A key case study is the “Wake” sculpture by conceptual artist Mel Chin, where the waterjet’s versatility and precision were crucial. The waterjet allowed for cutting a wide range of materials, maintaining material integrity, and streamlining prototyping and complex assembly processes. The project demonstrated the waterjet’s practical applications and its role in fostering interdisciplinary collaboration. The chapter concludes by highlighting the waterjet’s impact on holistic education, providing students with hands-on experience and preparing them for future challenges in art and engineering. The STEAM Studio’s success with the waterjet serves as a model for integrating advanced technology into university maker spaces.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.533
Threshold uncertainty score1.000

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.001

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 head0.008
GPT teacher head0.214
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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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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