MétaCan
Menu
Back to cohort
Record W2918385368 · doi:10.1080/13621718.2019.1584455

Dimensionless representation of the column characteristics and weld pool interactions for a DC argon arc

2019· article· en· W2918385368 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

VenueScience and Technology of Welding & Joining · 2019
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsUniversity of Alberta
FundersDirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de MéxicoConsejo Nacional de Ciencia y Tecnología
KeywordsArc (geometry)Dimensionless quantityMaterials scienceMechanicsPlasma arc weldingArgonCurrent (fluid)Gas tungsten arc weldingElectric arcWeldingThermodynamicsArc weldingAtomic physicsComposite materialGeometryPhysicsElectrodeMathematics

Abstract

fetched live from OpenAlex

The column characteristics and weld pool interactions for a DC argon arc in a GTAW configuration are represented by dimensionless correlations. Arc was described by a numerical model, considering currents between 200–300 A and arc lengths between 5–10 mm. The arc, defined as the loci of the 10,000 K isotherm, was found to display a parabolic shape; whose parameters are independent of current and arc length. Arc column temperature, velocity, and magnetic field are scaled with the width of the arc and a maximum that can also be predicted with a dimensionless approach. Arc-melt interactions include arc pressure, heat and current fluxes, and shear stress are also captured in dimensionless form. Scaling results compare well to numerical and experimental results reported.

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 score0.086
Threshold uncertainty score0.155

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 head0.012
GPT teacher head0.256
Teacher spread0.245 · 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