The Effect of Skelp Thickness on Precipitate Size and Morphology for X70 Microalloyed Steel Using Rietveld Refinement (Quantitative X-ray Diffraction)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".