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Rosette Nanotubes for Targeted Drug Delivery

2011· other· en· W1584055610 on OpenAlexaff
Sarabjeet Singh Suri, Hicham Fenniri, Baljit Singh

Bibliographic record

VenueNanotechnologies for the Life Sciences · 2011
Typeother
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsNational Institute for NanotechnologyUniversity of AlbertaUniversity of Saskatchewan
Fundersnot available
KeywordsReceptorRosette (schizont appearance)PeptideAdenylate kinaseCell biologyMaterials scienceNanotechnologyBiologyBiochemistryImmunology

Abstract

fetched live from OpenAlex

Abstract The sections in this article are Introduction Peptide‐Based Nanotubes Self‐Assembling Rosette Nanotubes Self‐Assembly Peptides G∧CMotif Self‐Assembly: Novel Helical Rosette Nanotubes Novelty G∧CMotif Self‐Assembly Process Built‐In Strategy for Manipulating the Properties ofRNTs Biological Functions ofRNTs Stability Issues Nanomaterials for Receptor‐Mediated Targeting Human Epidermal Growth Factor Receptor ( EGFR ) Vasoactive Pituitary Adenylate Cyclase (VPAC)‐Activating Peptide Receptors Transferrin Receptor (TfR) Folate Receptor (FR) Ethical Issues and Future Directions Conclusions

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.031
GPT teacher head0.265
Teacher spread0.235 · 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
Published2011
Admission routes1
Has abstractyes

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