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Record W4312278175 · doi:10.31399/asm.cp.itsc2009p1151

Cold Spraying Combined with Laser Surface Pre-Treatment Using Protal

2009· article· en· W4312278175 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThermal spray · 2009
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceGas dynamic cold sprayCoatingSubstrate (aquarium)LaserAdhesionFOIL methodAluminiumIrradiationComposite materialAluminum foilPulsed laser depositionDeposition (geology)Thin filmMetallurgyOpticsLayer (electronics)Nanotechnology

Abstract

fetched live from OpenAlex

Abstract In this study, fine aluminum powder was cold sprayed onto aluminum substrates, some of which were polished, some grit blasted, and some pretreated using a nano-pulsed Nd:YAG laser. In the latter case, the laser is coupled with the cold spray gun and the irradiation treatment occurs just prior to deposition. To better understand the interaction mechanisms involved with laser pretreating, coating-substrate interfaces were examined on thin-foil specimens and adhesion strength was determined by laser shock testing. The results show that substrate pretreatment with a nano-pulsed laser significantly improves the coating-substrate interface as well as coating adhesion.

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.

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 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 score0.137
Threshold uncertainty score0.894

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)0.0000.000

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 head0.011
GPT teacher head0.231
Teacher spread0.220 · 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