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Record W2030148427 · doi:10.1179/174329306x148200

Experimental examination of welding nozzle jet flow at cold flow conditions

2006· article· en· W2030148427 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueScience and Technology of Welding & Joining · 2006
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGas metal arc weldingShielding gasNozzleWeldingMaterials scienceFlow (mathematics)MechanicsJet (fluid)Particle image velocimetryFlow visualizationGas tungsten arc weldingElectromagnetic shieldingWeld poolMechanical engineeringMetallurgyLaser beam weldingArc weldingComposite materialTurbulenceEngineeringPhysics

Abstract

fetched live from OpenAlex

A fundamental study of welding nozzle flow under cold flow conditions is presented. The aim is to examine the shielding gas flow characteristics for several gas metal arc welding (GMAW) flow conditions. Experimental investigations are used to predict the flow behaviour of the gas shielding the weld pool. Results are presented for generic GMAW nozzle configurations at typical welding situations under cold flow. Flow visualisation and particle image velocimetry (PIV) gas velocity measurements reveal the various flow characteristics that are crucial to understanding of weld pool protection by the shielding gas. Results may be used to assist in understanding shield gas delivery and the effect of flow and geometry variables on shield gas coverage.

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 score0.014
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.008
GPT teacher head0.231
Teacher spread0.223 · 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