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Record W2538058561 · doi:10.1130/abs/2016am-281960

MINERALS WITH METAL-ORGANIC FRAMEWORK STRUCTURES

2016· article· en· W2538058561 on OpenAlexafffund
Sergey V. Krivovichev, Igor Huskić, Igor V. Pekov, Tomislav Friščić

Bibliographic record

VenueAbstracts with programs - Geological Society of America · 2016
Typearticle
Languageen
FieldEngineering
TopicIndustrial Engineering and Technologies
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesUniversidad de los AndesSaint Petersburg State UniversityGeorgetown UniversityNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsComputer science

Abstract

fetched live from OpenAlex

Metal-organic frameworks (MOFs) are an increasingly important family of advanced materials based on open, nanometer-scale metal-organic architectures, whose design and synthesis are based on the directed assembly of carefully designed subunits.We now demonstrate an unexpected link between mineralogy and MOF chemistry by discovering that the rare organic minerals stepanovite and zhemchuzhnikovite exhibit structures found in well-established magnetic and proton-conducting metal oxalate MOFs.Structures of stepanovite and zhemchuzhnikovite, exhibiting almost nanometer-wide and guest-filled apertures and channels, respectively, change the perspective of MOFs as exclusively artificial materials and represent, so far, unique examples of open framework architectures in organic minerals.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.216
Teacher spread0.202 · 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

Citations1
Published2016
Admission routes2
Has abstractyes

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