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Record W2958086973 · doi:10.1183/13993003.00795-2019

Same meat, different gravy: ignore the new names of mycobacteria

2019· letter· en· W2958086973 on OpenAlexaff
Enrico Tortoli, Barbara A. Brown‐Elliott, James D. Chalmers, Daniela María Cirillo, Charles L. Daley, Stefan Emler, R. Andrés Floto, María José García Sánchez, Wouter Hoefsloot, Won‐Jung Koh, Christoph Lange, Michael R. Loebinger, Florian P. Maurer, Kozo Morimoto, Stefan Niemann, Elvira Richter, Christine Y. Turenne, Ravikiran Vasireddy, Sruthi Vasireddy, Dirk Wagner, Richard J. Wallace, Nancy L. Wengenack, Jakko van Ingen

Bibliographic record

VenueEuropean Respiratory Journal · 2019
Typeletter
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsConfusionNomenclatureHarmSynonym (taxonomy)MycobacteriumBiologyGenealogyZoologyTaxonomy (biology)HistoryPsychologyGenusGeneticsSocial psychologyBacteria

Abstract

fetched live from OpenAlex

<b>A new name for most <i>Mycobacterium</i> species has been recently proposed. According to taxonomic rules novel and previous nomenclature coexist and are synonyms. The use of the latter has the advantage of avoiding confusion for healthcare and harm for patients.</b>http://bit.ly/2XhTACc

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient 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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.396
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.041
GPT teacher head0.282
Teacher spread0.241 · 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
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

Citations72
Published2019
Admission routes1
Has abstractyes

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