MétaCan
Menu
Back to cohort
Record W6931543100 · doi:10.5281/zenodo.6048672

Fig. 1 in Emendation of the Genus Auritidibacter Yassin et al. 2011 and Auritidibacter ignavus Yassin et al. 2011 based on features observed from Canadian and Swiss clinical isolates and wholegenome sequencing analysis

2020· other· en· W6931543100 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typeother
Languageen
FieldMathematics
TopicStatistical Methods in Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsPhylogenetic treePhylogeneticsMaximum parsimonyType (biology)GenusTree (set theory)CladeDNA sequencing

Abstract

fetched live from OpenAlex

Fig. 1. Phylogenetic consensus tree based on nearly full-length 16S rRNA gene sequences. Phylogeny reconstructed with neighbourjoining (NJ), maximum-likelihood (ML) and maximum parsimony algorithms with NJ/ML/MP data respectively shown at nodes for type strains of type species of genera assigned to the family Micrococcaceae. The tree was calculated using MEGA X based on 1000 resamplings (bootstrap analysis, with only values ≥70% shown). Bar, 0.01 (NJ) or 0.02 (ML) substitutions per nucleotide position. The type strain of Cellulomonas flavigena was included as an unrelated outlier.

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)0.000
metaresearch head score (Gemma)0.001
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: Other · Consensus signal: Other
Teacher disagreement score0.064
Threshold uncertainty score0.215

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0640.014

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 head0.148
GPT teacher head0.361
Teacher spread0.213 · 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
GenreOther

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

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicStatistical Methods in EpidemiologyFrench-language works237,207