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Fungal nomenclature evolving: changes adopted by the 19th International Botanical Congress in Shenzhen 2017, and procedures for the Fungal Nomenclature Session at the 11th International Mycological Congress in Puerto Rico 2018

2017· article· en· W2759108977 on OpenAlexaff
David L. Hawksworth, Tom W. May, Scott A. Redhead

Bibliographic record

VenueIMA Fungus · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsAgriculture and Agri-Food Canada
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsNomenclatureSession (web analytics)Library scienceChinaLawPolitical scienceHistoryBiologyComputer scienceEcologyTaxonomy (biology)World Wide Web

Abstract

fetched live from OpenAlex

This article summarizes the key changes in the rules relating to the nomenclature of fungi made at the XIX International Botanical Congress in Shenzhen, China, in July 2017. Most significant was the decision to transfer decision-making on matters related only to the naming of fungi from International Botanical to International Mycological Congresses (IMCs). The rules relating to fungi are to be grouped together in a separate section of the International Code of Nomenclature for algae, fungi, and plants (ICN). The way in which the Fungal Nomenclature Session will operate at the upcoming IMC in Puerto Rico in 2018 is summarized and the timetable for new proposals is presented. In addition, approval for names included on lists of protected names to be protected against unlisted as well as known competing names were passed, as were some simplifications relating to the naming of pleomorphic fungi. From 1 January 2019, it will also be necessary to deposit details of lecto-, neo-, and epitypifications in one of the recognized repositories of fungal names in order for them to be validly published and to establish their priority. Various aspects relating to typifications were referred to a new Special Committee, with a separate Special Subcommittee charged with addressing the issue of using DNA sequences as types for all groups covered by the ICN. It is anticipated that the Shenzhen Code will be published in the first half of 2018.

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)0.018
metaresearch head score (Gemma)0.015
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: Empirical · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0070.004
Scholarly communication0.0060.003
Open science0.0030.004
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0110.006

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 head0.284
Teacher spread0.267 · 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
GenreEmpirical

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

Citations30
Published2017
Admission routes1
Has abstractyes

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