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Record W4399544265 · doi:10.32614/cran.package.anovir

anovir: Analysis of Virulence

2020· dataset· en· W4399544265 on OpenAlexfundno aff
Philip Agnew, Jimmy Lopez

Bibliographic record

Venuenot available
Typedataset
Languageen
FieldMedicine
TopicParvovirus B19 Infection Studies
Canadian institutionsnot available
FundersCentre National de la Recherche ScientifiqueInstitute of Cancer ResearchInstitut de Recherche pour le Développement
KeywordsVirulenceBiologyGeneticsGene

Abstract

fetched live from OpenAlex

Epidemiological population dynamics models traditionally define a pathogen's virulence as the increase in the per capita rate of mortality of infected hosts due to infection. This package provides functions allowing virulence to be estimated by maximum likelihood techniques. The approach is based on the analysis of relative survival comparing survival in matching cohorts of infected vs. uninfected hosts (Agnew 2019) &lt;<a href="https://doi.org/10.1101%2F530709" target="_top">doi:10.1101/530709</a>&gt;.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.008
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.044
GPT teacher head0.355
Teacher spread0.311 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2020
Admission routes1
Has abstractyes

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