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Record W2735659788 · doi:10.1038/aps.2017.64

Section 1: Neuropharmacology

2017· article· en· W2735659788 on OpenAlexfundno aff
Pei Wan, Linlin Liu, Uma Gau, Wenhua Zheng, Ni Pan, Liuyi Lu, Jie Liu, Guan Yong-yuan, Guan‐Lei Wang, Jianhua Ding, Juan Ji, Ding Xu, Hui Yan, Xiu‐Lan Sun, Yinfeng Dong, Zheng‐Zhen Chen, Zhan Zhao, Dandan Yang, Qian Ren, Cong‐Yuan Xia, Zhen‐Zhen Wang, Nai‐Hong Chen

Bibliographic record

VenueActa Pharmacologica Sinica · 2017
Typearticle
Languageen
FieldNeuroscience
TopicAnesthesia and Neurotoxicity Research
Canadian institutionsnot available
FundersNorthwest Fisheries Science CenterNational Science and Technology Major ProjectNatural Sciences and Engineering Research Council of CanadaJiangxi University of Traditional Chinese MedicineCanadian Institutes of Health ResearchDepartment of Education of Guangdong ProvinceState Key Laboratory of Bioactive Substance and Function of Natural MedicinesChinese Academy of Medical SciencesNational Institutes of HealthPeking Union Medical CollegeBeijing Institute of TechnologyHealth Commission of Jiangxi ProvinceNatural Science Foundation of Jiangxi ProvinceFundo para o Desenvolvimento das Ciências e da TecnologiaUniversidade de MacauNational Natural Science Foundation of ChinaJinan UniversityChinese Academy of SciencesHunan University of Chinese MedicineAcademy of Medical SciencesUniversity of ManitobaHeart and Stroke Foundation of Canada
KeywordsNeuropharmacologySection (typography)MedicineNeurosciencePharmacologyPsychologyComputer 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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.046
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0460.035

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 head0.165
GPT teacher head0.417
Teacher spread0.252 · 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

Citations1
Published2017
Admission routes1
Has abstractno

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