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Record W2939734479 · doi:10.1094/pdis-03-19-0454-a

The CpnClassiPhyR Is a Resource for <i>cpn</i>60 Universal Target-Based Classification of Phytoplasmas

2019· article· en· W2939734479 on OpenAlexafffund
Kevin Muirhead, Edel Pérez‐López, Brian W. Bahder, Janet E. Hill, Tim Dumonceaux

Bibliographic record

VenuePlant Disease · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytoplasmas and Hemiptera pathogens
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Saskatchewan
FundersAgriculture and Agri-Food CanadaWestern Canada Research GridCompute CanadaUniversity of Saskatchewan
KeywordsPhytoplasmaBiologyRestriction fragment length polymorphismGeneticsPhylogenetic tree16S ribosomal RNARibosomal RNAIn silicoGeneComputational biologyPolymerase chain reaction

Abstract

fetched live from OpenAlex

Phytoplasmas are plant-pathogenic bacteria that are associated with yield losses in many crop plants worldwide. Phytoplasma strain differentiation is accomplished using in silico restriction fragment length polymorphism (RFLP) analysis of 16S ribosomal RNA-encoding gene sequences, which has resulted in the definition of ribosomal groups and subgroups of phytoplasmas. Due to limitations associated with this approach, a complementary classification scheme was recently developed based on RFLP analysis of the single-copy, protein-encoding gene chaperonin-60 (cpn60). We present the CpnClassiPhyR, software that facilitates phytoplasma strain classification using both RFLP and automated phylogenetic analysis of cpn60 sequences. This software is available through a web interface at http://cpnclassiphyr.ca .

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)0.002
metaresearch head score (Gemma)0.006
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: Not applicable
GenreCandidate signal: Software · Consensus signal: Software
Teacher disagreement score0.030
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.003
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0300.024

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 head0.017
GPT teacher head0.202
Teacher spread0.185 · 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
GenreSoftware

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

Citations20
Published2019
Admission routes2
Has abstractyes

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