MétaCan
Menu
Back to cohort
Record W104831067 · doi:10.2172/1024123

Calix 2007:9th International Conference on Calixarene Chemistry

2011· report· en· W104831067 on OpenAlexaboutno aff
Jeffery Davis

Bibliographic record

Venuenot available
Typereport
Languageen
FieldChemistry
TopicSupramolecular Chemistry and Complexes
Canadian institutionsnot available
Fundersnot available
KeywordsCalixareneChemistrySupramolecular chemistryScope (computer science)Library scienceNanotechnologyOrganic chemistryComputer scienceMaterials science

Abstract

fetched live from OpenAlex

The DOE funds helped support an International Conference, Calix 2007, whose focus was on Supramolecular Chemistry. The conference was held at the University of Maryland from August 6-9, 2007 (Figure 1). The conference website is at www.chem.umd.edu/Conferences/Calix2007. This biannual conference had previously been held in the Czech Republic (2005), Canada (2003), Netherlands (2001), Australia (1999), Italy (1997), USA (Fort Worth, 1995) Japan (1993) and Germany (1991). Calixarenes are cup-shaped compounds that are a major part of Supramolecular Chemistry, for which Cram, Lehn and Pederson were awarded a Nobel Prize 20 years ago. Calixarene chemistry has expanded greatly in the last 2 decades, as these compounds are used in synthetic and mechanistic chemistry, separations science, materials science, nanoscience and biological chemistry. The organizing committee was quite happy that Calix 2007 encompassed the broad scope and interdisciplinary nature of the field. Our goal was to bring together leading scientists interested in calixarenes, molecular recognition, nanoscience and supramolecular chemistry. We believe that new research directions and collaborations resulted from an exchange of ideas between conferees. This grant from the DOE was crucial toward achieving that goal, as the funds helped cover some of the registration and accommodations costs for the speakers.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.275
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2750.000

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 head0.100
GPT teacher head0.311
Teacher spread0.212 · 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 teacher head, not a consensus.

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
Published2011
Admission routes1
Has abstractyes

Explore more

Same topicSupramolecular Chemistry and ComplexesFrench-language works237,207