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Record W3048248898 · doi:10.1038/s41563-020-0755-z

The dose threshold for nanoparticle tumour delivery

2020· article· en· W3048248898 on OpenAlexafffund
Ben Ouyang, Wilson Poon, Yi-Nan Zhang, Zachary P. Lin, Benjamin R. Kingston, Anthony J. Tavares, Yuwei Zhang, Juan Chen, Michael S. Valic, Abdullah M. Syed, Presley MacMillan, Julien Couture‐Senécal, Gang Zheng, Warren C. W. Chan

Bibliographic record

VenueNature Materials · 2020
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity Health NetworkPrincess Margaret Cancer CentreSheridan CollegeToronto Rehabilitation InstituteUniversity of Toronto
FundersCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchTerry Fox Research InstituteCanada Research ChairsGovernment of CanadaNatural Sciences and Engineering Research Council of CanadaPrincess Margaret Cancer Foundation
KeywordsNanoparticleNanotechnologyDrug deliveryMaterials scienceMedicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.003
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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0080.003

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 head0.016
GPT teacher head0.251
Teacher spread0.236 · 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

Citations498
Published2020
Admission routes2
Has abstractno

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