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Record W3187375978 · doi:10.1007/s13225-021-00481-x

Defining a species in fungal plant pathology: beyond the species level

2021· article· en· W3187375978 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFungal Diversity · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBiologySubspeciesSpecies complexSpecies nameIdentification (biology)ConfusionEcologyEvolutionary biologyTaxonomy (biology)Phylogenetic treeGenetics

Abstract

fetched live from OpenAlex

In plant pathology, the correct naming of a species is essential for determining the causal agents of disease. Species names not only serve the general purpose of concise communication, but also are critical for effective plant quarantine, preventing the introduction of new pathogens into a territory. Many phytopathogenic genera have multiple species and, in several genera, disagreements between the multiple prevailing species concept definitions result in numerous cryptic species. Some of these species were previously called by various names; forma speciales (specialised forms), subspecies, or pathotypes. However, based on new molecular evidence they are being assigned into new species. The frequent name changes and lack of consistent criteria to delineate cryptic species, species, subspecies, forms, and races create increasing confusion, often making communication among biologists arduous. Furthermore, such ambiguous information can convey misleading evolutionary concepts and species boundaries. The aim of this paper is to review these concepts, clarify their use, and evaluate them by referring to existing examples. We specifically address the question, “Do plant pathogens require a different ranking system?” We conclude that it is necessary to identify phytopathogens to species level based on data from multiple approaches. Furthermore, this identification must go beyond species level to clearly classify hitherto known subspecies, forms and races. In addition, when naming phytopathogenic genera, plant pathologists should provide more information about geographic locations and host ranges as well as host specificities for individual species, cryptic species, forms or races. When describing a new phytopathogen, we suggest that authors provide at least three representative strains together with pathogenicity test results. If Koch’s postulates cannot be fulfilled, it is necessary to provide complementary data such as associated disease severity on the host plant. Moreover, more sequenced collections of species causing diseases should be published in order to stabilise the boundaries of cryptic species, species, subspecies, forms, and races.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.210
Teacher spread0.183 · 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