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Adenoviral vector based new tuberculosis vaccine Ad5Ag85A

2013· article· en· W3032101849 on OpenAlexaff
邵忠琦, Zhou Xing, Z. TAO, 宇学峰

Bibliographic record

VenueZhonghua weishengwuxue he mianyixue zazhi · 2013
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcMaster University
Fundersnot available
KeywordsVirologyVector (molecular biology)TuberculosisTuberculosis vaccinesMedicineMycobacterium tuberculosisBiologyRecombinant DNAGeneticsPathologyGene

Abstract

fetched live from OpenAlex

结核病是一个在全球范围内严重威胁人类健康的呼吸道传染性疾病.据世界卫生组织(WHO)估计,世界上约有1/3的人口受到结核分枝杆菌(Mycobacterium tuberculosis)的感染,其中1/10的人可能会最终患上结核病.全球每年的新病例超过900万,约200万人死于结核病[1].结核病负担最重的地区主要是东南亚(占总报告病例的35%)、非洲(24%)和西太平洋地区(22%).中国的结核病年发病患者数约为130万,占全球发病的14.3%,是世界第二大结核病高发国家(仅次于印度)[2]。

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 score0.003
Threshold uncertainty score0.012

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.020
GPT teacher head0.284
Teacher spread0.264 · 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
Published2013
Admission routes1
Has abstractyes

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