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Record W2163137878 · doi:10.1139/v09-086

2008 Macromolecular Science and Engineering Division Award Lecture — Conjugated polymers: From micro-electronics to genomics

2009· article· en· W2163137878 on OpenAlexaffvenue
Mario Leclerc, Ahmed Najari, Serge Beaupré

Bibliographic record

VenueCanadian Journal of Chemistry · 2009
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPolymerOligonucleotideChemistryNucleic acidConjugated systemPolythiopheneNanotechnologyMacromoleculeCombinatorial chemistryCationic polymerizationDNAOrganic chemistryConductive polymerMaterials scienceBiochemistry

Abstract

fetched live from OpenAlex

Conjugated polymers have received a lot of attention, since they combine the best features of metals or semiconductors with those of synthetic polymers. For instance, solar cells based on poly(2,7-carbazole) derivatives have revealed power-conversion efficiencies up to 6%. This class of materials could lead to printable and flexible photovoltaic devices. Moreover, water-soluble luminescent polythiophenes have allowed the specific, rapid, and ultra-sensitive detection of unlabelled DNA, RNA, or proteins. This new optical detection mechanism is based on electrostatic interactions between a cationic polythiophene derivative and negatively charged oligonucleotides. This method makes now possible the rapid assessment of the identity of single nucleotide polymorphisms (SNPs), genes, and pathogens without the need for nucleic acid amplification.

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)0.000
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.068
Threshold uncertainty score0.226

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0680.029

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 head0.005
GPT teacher head0.199
Teacher spread0.194 · 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

Citations6
Published2009
Admission routes2
Has abstractyes

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