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Record W1491081422 · doi:10.1107/97809553602060000741

Core dictionary (coreCIF)

2006· book-chapter· en· W1491081422 on OpenAlexaff
S. R. Hall, Frank H. Allen, I. D. Brown

Bibliographic record

VenueInternational Tables for Crystallography · 2006
Typebook-chapter
Languageen
FieldMaterials Science
TopicX-ray Diffraction in Crystallography
Canadian institutionsMcMaster UniversityBrockhouse Institute for Materials Research
Fundersnot available
KeywordsCore (optical fiber)Computer scienceSet (abstract data type)Information retrievalData setUnit (ring theory)CrystallographyNatural language processingArtificial intelligenceChemistryMathematicsProgramming language

Abstract

fetched live from OpenAlex

This is version 2.3.1 of the core CIF dictionary (coreCIF). A commentary on the use of this dictionary may be found in Chapter 3.2. The data names defined here are central to the description and reporting of any crystal-structure determination, and this dictionary collects the natural set of descriptors for small-unit-cell structures (typically inorganic or small-molecule organic or metal-organic compounds) determined in single-crystal experiments. Keywords: crystallography; CIF; CIF dictionaries; Crystallographic Information File; coreCIF; core Crystallographic Information File; data names; data categories; DDL1

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)0.003
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.406
Threshold uncertainty score0.848

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0110.019
Science and technology studies0.0020.001
Scholarly communication0.0070.009
Open science0.0060.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.4060.400

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 head0.044
GPT teacher head0.275
Teacher spread0.232 · 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.

Study designNot applicable
Domainnot available
GenreMethods

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

Citations0
Published2006
Admission routes1
Has abstractyes

Explore more

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