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Record W4229508199 · doi:10.26434/chemrxiv.5592715.v1

Three-Dimensional Printing of Ellipsoidal Structures Using Mercury

2017· preprint· en· W4229508199 on OpenAlexaff
Matthew Brown, Ken Van Wieren, Hamel N. Tailor, David Hartling, Anthony Jean, Nabyl Merbouh

Bibliographic record

VenueChemRxiv · 2017
Typepreprint
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsDepictionSoftwareComputer scienceMercury (programming language)Three dimensional printingComputer graphics (images)File format3D printingEngineering drawingEllipsoidAnisotropyMechanical engineeringDatabaseEngineeringProgramming languageArtVisual artsGeologyOpticsPhysics

Abstract

fetched live from OpenAlex

A description of how to use the Mercury software from the CCDC to print 3-dimensional crystal structures that depict the anisotropic displacement parameters, matching the commonly used ellipsoidal depiction used in scientific papers. Details on how to convert a cif file into a 3D printing data file is included in the main paper, and details on the preparation of that data file for printing on a number of different 3D printers is included in the ESI.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0400.016

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 head0.036
GPT teacher head0.259
Teacher spread0.223 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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
Published2017
Admission routes1
Has abstractyes

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