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Record W2880058096 · doi:10.3390/cryst8070287

The Effect of Skelp Thickness on Precipitate Size and Morphology for X70 Microalloyed Steel Using Rietveld Refinement (Quantitative X-ray Diffraction)

2018· article· en· W2880058096 on OpenAlex
Corentin Chatelier, J. B. Wiskel, Douglas G. Ivey, H. Henein

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCrystals · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceRietveld refinementVolume fractionDiffractionTinMetallurgyAnalytical Chemistry (journal)PrecipitationMass fractionMicroalloyed steelMicrostructureComposite materialAusteniteChemistryChromatography

Abstract

fetched live from OpenAlex

Precipitates in thin-walled (11 mm) and thick-walled X70 (17 mm) microalloyed X70 pipe steel are characterized using Rietveld refinement (a.k.a. quantitative X-ray diffraction (QXRD)), inductively coupled plasma mass spectrometry (ICP), and energy-dispersive X-ray spectroscopy (EDX) analyses. Rietveld refinement is done to quantify the relative abundance, compositions, and size distribution of the precipitates. EDX and ICP analyses are undertaken to confirm Rietveld refinement analysis. The volume fraction of large precipitates (1 to 4 μm—mainly TiN rich precipitates) is determined to be twice as high in the thick-walled X70 steel (0.07%). Nano-sized precipitates (<20 nm) in the thin-walled steel exhibit a higher volume fraction (0.113%) than in the thick-walled steel (0.064%). The compositions of the nano-sized precipitates are similar for both steels.

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 score0.441

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 head0.015
GPT teacher head0.259
Teacher spread0.244 · 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