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Record W2735513153 · doi:10.1038/aps.2017.69

Section 6: Pharmaceutics, Pharmacokinetics, Drug Discovery

2017· article· en· W2735513153 on OpenAlexafffund
Feifei Gao, Chao-Yang Chen, Xiao Liu, Ying Zhou, Yimin Cui, Ye Wan, Jianli Chen, Xiaoyan Yu, Liang Liu, Xiaoming Zhu, Lijuan Wei, Bin Guo, Zongchao Jia, Shengjun Fan, Xuejun Li, Wanping Zhong, Hongmin Wu, Jiyan Chen, Xinxin Li, Haoming Lin, Bin Zhang, Zhiwei Zhang, Dun-Liang Ma, Shuo Sun, Hanping Li, Liping Mai, Guodong He, Xipei Wang, Heping Lei, Huangkai Zhou, Lan Tang, Shuwen Liu, Shilong Zhong

Bibliographic record

VenueActa Pharmacologica Sinica · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBiosimilars and Bioanalytical Methods
Canadian institutionsQueen's University
FundersScience and Technology Planning Project of Guangdong ProvinceCentre for Addiction and Mental HealthChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaMinistry of Education of the People's Republic of ChinaNational Natural Science Foundation of ChinaNingxia UniversityHigher Education Discipline Innovation ProjectUniversité de SherbrookeNingxia Medical UniversityChina Medical UniversityHeart and Stroke Foundation of CanadaCanadian Institutes of Health ResearchAmerican Heart AssociationLeslie Dan Faculty of Pharmacy, University of TorontoHuashan HospitalFundo para o Desenvolvimento das Ciências e da TecnologiaMichael J. Fox Foundation for Parkinson's Research
KeywordsPharmaceuticsPharmacokineticsDrugPharmacologySection (typography)MedicineDrug discoveryBioinformaticsBiologyComputer science

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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: none
Teacher disagreement score0.958
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0420.028

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 head0.060
GPT teacher head0.381
Teacher spread0.320 · 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.

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
Published2017
Admission routes2
Has abstractno

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