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Record W2019913436 · doi:10.1115/pvp2013-97299

Low-Constraint Toughness Testing: Results of a Round Robin on a Draft SE(T) Test Procedure

2013· article· en· W2019913436 on OpenAlexaff
W. R. Tyson, J. A. Gianetto

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsToughnessGirth (graph theory)Tension (geology)Round robin testStructural engineeringConstraint (computer-aided design)Materials scienceBendingEnhanced Data Rates for GSM EvolutionWeldingComposite materialComputer scienceMathematicsEngineeringMechanical engineeringUltimate tensile strengthStatistics

Abstract

fetched live from OpenAlex

Assessment of the effect of girth weld flaws on pipeline integrity requires knowledge of a number of factors: pipe geometry, applied loads, flaw size, and pipe mechanical properties. Of the latter, strength and toughness are the primary factors. Toughness has conventionally been measured using specimens tested in bending to maximize constraint. While this gives a conservative estimate of toughness, it would be better to use a test that would reveal the toughness in constraint conditions typical of girth weld flaws: namely, relatively shallow flaws loaded in tension. Consequently, there is a trend to evaluate toughness using pre-cracked single-edge-cracked tension (i.e. SE(T)) specimens, and one procedure has already been standardized. However, this procedure requires the use of multiple specimens to generate a resistance curve. With the objective of devising a more economical test, a single-specimen procedure has been developed at CANMET. The viability of this procedure has been assessed by means of a round robin involving test and research laboratories from around the world. In this presentation, the results of the round robin will be presented and discussed.

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)0.000
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 score0.233
Threshold uncertainty score0.594

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 head0.018
GPT teacher head0.215
Teacher spread0.197 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations13
Published2013
Admission routes1
Has abstractyes

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