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Dry Ice Cleaning of Electrical Equipment

2023· article· en· W4408899096 on OpenAlexaff
John Kay, Graham Green

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical and Thermal Properties Analysis
Canadian institutionsKruger (Canada)Rockwell Automation (Canada)
Fundersnot available
KeywordsEnvironmental scienceDry iceDry cleaningComputer scienceMaterials scienceEngineeringWaste managementComposite material

Abstract

fetched live from OpenAlex

Dry ice blast cleaning originated in the aircraft industry when they were looking for alternative ways to strip paint off older aircraft, at that time. The technology did not become commercially available until around 1987. The dry ice cleaning process begins with the creation and use of pellet or granular shapes made from liquid Carbon Dioxide (CO2).This type of cleaning method is non-abrasive and does no damage to the base substrate materials. Therefore, it can even be used on sensitive mechanical and electrical equipment. The process is not electrically conductive and can be safely used on electric motors and other electrical equipment.Dry ice exists as a liquid only when under very high pressure. When the pressure drops to near normal atmospheric pressure, approximately half of the dry ice turns back into a gaseous form and half turns to a solid. These solids, usually in the form of fluffy snow-like material, are then compressed to form dry ice blocks, pellets, or nuggets for use in the cleaning 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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.004

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 head0.016
GPT teacher head0.205
Teacher spread0.189 · 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 designNot applicable
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

Citations0
Published2023
Admission routes1
Has abstractyes

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