MétaCan
Menu
Back to cohort
Record W4405360366 · doi:10.1115/ipc2024-134106

Investigating Weld and HAZ Effects During SMAW of Pipe and Fitting Materials

2024· article· en· W4405360366 on OpenAlexaff
Nick Khotenko, Cory L McIntosh

Bibliographic record

VenueVolume 3: Operations, Monitoring, and Maintenance; Materials and Joining · 2024
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsATCO (Canada)
Fundersnot available
KeywordsWeldingShielded metal arc weldingMaterials scienceMetallurgyArc weldingGas metal arc welding

Abstract

fetched live from OpenAlex

Abstract The primary goal of weldments is to have the longitudinal strain not concentrated in the weld. To date, there is no reliable way to accurately predict this, given the variability of welding techniques, mechanical properties, and manufacturing methods of materials in CSA Z662 pipeline designs. Several girth welds were completed using SMAW on common carbon steel pipe and fittings used under CSA Z662 designs. The results focus on comparing as-welded joint properties relative to the base metal. The base materials were chosen to be wide ranging in chemistry and manufacturing process. The deposited weld filler metal tensile strength can be relatively and consistently predicted; however, the overall weldment properties are more difficult to estimate. The results provide an opportunity to learn more about the response of welding on the base metal. The goal is to improve the ability to design and complete weldments that have equal or better mechanical properties than the base metal. Advancements are already being made to new construction best practices, but there is value in understanding how conventional filler metals and base metals are allocated to highest risk welds for new construction and existing assets.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.064
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.225
Teacher spread0.216 · 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 teacher head, not a consensus.

Study designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueVolume 3: Operations, Monitoring, and Maintenance; Materials and JoiningSame topicNon-Destructive Testing TechniquesFrench-language works237,207