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Record W4385410801 · doi:10.33137/utmj.v100i2.41463

The knowledge-informed development of inhaled aerosol vaccine strategies

2023· article· en· W4385410801 on OpenAlexafffundvenue
Zhou Xing, Fiona Smaill

Bibliographic record

VenueUniversity of Toronto Medical Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health Research
KeywordsMedicineTuberculosisTuberculosis vaccinesCoronavirus disease 2019 (COVID-19)Intensive care medicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)ImmunologyVirologyInfectious disease (medical specialty)Mycobacterium tuberculosisDiseasePathology

Abstract

fetched live from OpenAlex

Respiratory infectious diseases including tuberculosis (TB), influenza, and COVID-19 account for significant morbidity and mortality worldwide. However, like most of the other human vaccines, the current-generation vaccines against these respiratory infections are administered parenterally via injection into the skin or muscle. As such, most of these human vaccines remain suboptimal in protection (Lavelle et al, 2022). This calls for developing next-generation vaccine strategies which are expected to perform above and beyond the current-generation vaccines.

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)0.009
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0020.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0140.005

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 head0.331
Teacher spread0.301 · 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 designTheoretical or conceptual
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
Published2023
Admission routes3
Has abstractyes

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