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Record W2944804715 · doi:10.1002/chem.201982763

Frontispiece: π‐Concave Hosts for Curved Carbon Nanomaterials

2019· paratext· en· W2944804715 on OpenAlexaff
Serxho Selmani, Derek J. Schipper

Bibliographic record

VenueChemistry - A European Journal · 2019
Typeparatext
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNanotechnologyHost (biology)FullereneNanomaterialsCarbon fibersMatching (statistics)Carbon nanotubeComputer scienceMaterials scienceChemistryBiologyMathematicsEcologyAlgorithm

Abstract

fetched live from OpenAlex

π-Concave hosts have proven to be an invaluable tool in the purification and application of fullerenes and carbon nanotubes. The unique host–guest shape matching has created a field of research that is dedicated to understanding and maximizing these interactions. Through slight alterations in host design, profound differences in binding emerge. In their Minireview on page 6673 ff., S. Selmani and D. J. Schipper shed light on the dynamic effects of these various changes by providing an overview of the current literature. Furthermore, they offer a checklist of the various factors that can be used to influence the binding of π-concave hosts with π-convex carbon nanomaterials.

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)0.001
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.146
Threshold uncertainty score0.490

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.1460.105

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 head0.022
GPT teacher head0.278
Teacher spread0.256 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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