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Record W2899662416 · doi:10.25011/cim.v41i2.31416

HIV Cure Research: An example of successful advocacy by scientists for science

2018· article· en· W2899662416 on OpenAlexafffundvenueabout
Jonathan B. Angel

Bibliographic record

VenueClinical and investigative medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersDepartment of Medicine, Ottawa HospitalOttawa Hospital Research InstituteCanadian Institutes of Health ResearchUniversity of OttawaCanadian Foundation for AIDS Research
KeywordsHuman immunodeficiency virus (HIV)Medical researchNew englandMedicineFamily medicineMedical scienceMedical educationMedical schoolPolitical sciencePathology

Abstract

fetched live from OpenAlex

Following medical school and an internal medicine residency in Toronto, and infec-tious diseases training at the New England Medical Center/Tufts University in Boston, Jonathan joined the Division of Infectious Diseases, Department of Medicine at the Ottawa General Hospital in 1995. His research focuses on understanding how HIV damages the immune system and how these insights may lead to new therapies. Jon-athan is currently Professor of Medicine, University of Ottawa and Senior Scientist, Ottawa Hospital Research Institute. He was Editor-in-Chief of CIM from 2010-2015.

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)0.112
metaresearch head score (Gemma)0.095
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.888
Threshold uncertainty score0.593

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1120.095
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.003
Science and technology studies0.0250.055
Scholarly communication0.0290.019
Open science0.0030.026
Research integrity0.0220.038
Insufficient payload (model declined to judge)0.0070.002

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 head0.474
GPT teacher head0.526
Teacher spread0.052 · 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
DomainIncentives
GenreCommentary

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
Published2018
Admission routes4
Has abstractyes

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