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Record W2746715358 · doi:10.1002/cpmc.36

Mouse Models of <i>Acinetobacter baumannii</i> Infection

2017· article· en· W2746715358 on OpenAlexaff
Greg Harris, Rhonda KuoLee, H. Howard Xu, Wangxue Chen

Bibliographic record

VenueCurrent Protocols in Microbiology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsBrock UniversityNational Research Council Canada
FundersProgram for Education and Research in Biotechnology, California State University
KeywordsAcinetobacter baumanniiBiologyVirulencePneumoniaPathogenSepsisMicrobiologyImmune systemImmunologyVirologyBacteriaGeneMedicinePseudomonas aeruginosaGeneticsInternal medicine

Abstract

fetched live from OpenAlex

This unit describes basic protocols for infecting mice through intranasal and intraperitoneal routes with Acinetobacter baumannii to induce associated pneumonia and sepsis, the two most common manifestations of clinical infections with this pathogen. By selecting the appropriate protocols and bacterial strains of different virulence, these mouse models provide an opportunity to study the infection pathogenesis and host-immune responses, and to evaluate the efficacies of prophylactic and therapeutic anti-A. baumannii candidates. © 2017 by John Wiley & Sons, Inc.

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)0.002
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: Methods · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.005

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 head0.039
GPT teacher head0.341
Teacher spread0.302 · 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
GenreMethods

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

Citations35
Published2017
Admission routes1
Has abstractyes

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