MétaCan
Menu
Back to cohort
Record W1995912116 · doi:10.1520/mpc20130030

Effect of Combined Cold Temperature and Fatigue Load on Performance of G40.21 Steel

2014· article· en· W1995912116 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

VenueMaterials Performance and Characterization · 2014
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMaterials scienceDuctility (Earth science)Ultimate tensile strengthComposite materialStructural engineeringDrop (telecommunication)CreepEngineering

Abstract

fetched live from OpenAlex

Abstract Many structures such as bridge decks and ship hulls are required to withstand fatigue load cycles throughout their service life. These structures are also required to withstand zero and subzero temperatures if located in northern and Arctic regions, where winter temperatures can drop down to −40°C. The combined effects of cold temperature and cyclic loads can lead to potential damage to the material performance and subsequent failure. In this study, CSA G40.21 350WT steel, which is typically used in ship building, was tested in strain-controlled fatigue load cycles to determine the effect of zero and subzero temperatures on the mechanical properties and fatigue life. The experimental results show a significant effect of temperature on the fatigue life of this steel. The tensile strength was not affected by low temperatures. The yield strength and fracture strength increased and the ductility decreased at low temperatures. This paper discusses the test procedure, test parameters, and test data obtained from this study.

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 score0.040
Threshold uncertainty score0.548

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 head0.004
GPT teacher head0.179
Teacher spread0.175 · 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