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Record W4220895472 · doi:10.1007/s11666-022-01363-7

How to Unleash the Remarkable Potential of Cold Spray: A Perspective

2022· article· en· W4220895472 on OpenAlexaff
Éric Irissou, Dominique Poirier, Phuong Vo, C. V. Cojocaru, Maniya Aghasibeig, Stephen Yue

Bibliographic record

VenueJournal of Thermal Spray Technology · 2022
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsMcGill UniversityNational Research Council Canada
Fundersnot available
KeywordsGas dynamic cold sprayCommercializationSoftware deploymentPerspective (graphical)NanotechnologySpray dryingMaterials scienceConsolidation (business)Systems engineeringEngineeringBusinessComputer scienceMarketing

Abstract

fetched live from OpenAlex

Abstract Cold spray is a solid-state, powder-based consolidation technique for deposition of coatings, component repair and near-net-shape additive manufacturing. Its unique attributes have propelled the development and commercialization, yet cold spray has only experienced limited deployment. In fact, cold spray technology could be extended to a considerably broader range of applications and achieve a much higher level of industry adoption by focusing on innovative ways to unlock current roadblocks that prevent it from reaching its full potential. Cold spray R&D efforts have doubled during the last decade and along with new industry applications and novel demands provide both a strong body of knowledge and market pull to identify and address these roadblocks. This paper offers the authors’ perspective on what are the next steps to be taken in cold spray R&D to unleash its remarkable potential.

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)0.004
metaresearch head score (Gemma)0.003
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: Commentary · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0050.009
Open science0.0010.002
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0080.002

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 head0.005
GPT teacher head0.207
Teacher spread0.202 · 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
GenreCommentary

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

Citations26
Published2022
Admission routes1
Has abstractyes

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