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Rietveld Refinement in the Characterization of Crystalline Materials

2019· book· en· W3125857707 on OpenAlexfundno aff
Igor Djerdj

Bibliographic record

Venuenot available
Typebook
Languageen
FieldMaterials Science
TopicX-ray Diffraction in Crystallography
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of Education, IndiaUniversity of JinanNational Research Foundation of KoreaHigher Education Discipline Innovation ProjectMinistry of Trade, Industry and EnergyKorea Institute of Energy Technology Evaluation and PlanningRussian Foundation for Basic ResearchNational Natural Science Foundation of ChinaNational Research Foundation
KeywordsRietveld refinementCharacterization (materials science)Materials scienceCrystallographyCrystal structureNanotechnologyChemistry

Abstract

fetched live from OpenAlex

This Special Issue serves as a crystallographic forum covering various aspects of material science that have in common the use of the powerful Rietveld method in the analysis of the powder XRD patterns of investigated compounds.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0300.041

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 head0.236
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations10
Published2019
Admission routes1
Has abstractyes

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