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Record W2155414077 · doi:10.1186/1742-4690-3-77

The cell biology of HIV-1 and other retroviruses

2006· review· en· W2155414077 on OpenAlexafffund
Eric O. Freed, Andrew J. Mouland

Bibliographic record

VenueRetrovirology · 2006
Typereview
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsJewish General HospitalMcGill University
FundersNational Cancer InstituteNational Institutes of HealthCanadian Institutes of Health ResearchAmerican Society for Cell Biology
KeywordsRetrovirusPresentation (obstetrics)Human immunodeficiency virus (HIV)BiologyVirologyLibrary scienceComputational biologyVirusMedicineComputer science

Abstract

fetched live from OpenAlex

In recognition of the growing influence of cell biology in retrovirus research, we recently organized a Summer conference sponsored by the American Society for Cell Biology (ASCB) on the Cell Biology of HIV-1 and other Retroviruses (July 20-23, 2006, Emory University, Atlanta, Georgia). The meeting brought together a number of leading investigators interested in the interplay between cell biology and retrovirology with an emphasis on presentation of new and unpublished data. The conference was arranged from early to late events in the virus replication cycle, with sessions on viral fusion, entry, and transmission; post-entry restrictions to retroviral infection; nuclear import and integration; gene expression/regulation of retroviral Gag and genomic RNA; and assembly/release. In this review, we will attempt to touch briefly on some of the highlights of the conference, and will emphasize themes and trends that emerged at the meeting. MEETING REPORT: The conference began with a keynote address from W. Sundquist on the biochemistry of HIV-1 budding. This presentation will be described in the section on Assembly and Release of Retroviruses.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.003

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 head0.032
GPT teacher head0.312
Teacher spread0.281 · 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
GenreReview

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

Citations59
Published2006
Admission routes2
Has abstractyes

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