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Surprising morphological, ecological and ITS sequence diversity in the Arrhenia acerosa complex (Basidiomycota: Agaricales: Hygrophoraceae)

2020· article· en· W6960043417 on OpenAlexaboutno aff

Bibliographic record

VenueDuo Research Archive (University of Oslo) · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBotanical Studies and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsTaxonGenetic diversityCircumscriptionSequence (biology)Diversity (politics)Species complexSubstrate (aquarium)

Abstract

fetched live from OpenAlex

A molecular genetic study of the Arrhenia acerosa complex using the ITS fungal barcoding marker revealed unexpected diversity along a cascading group supporting over 20 lineages. Among these, we identified five previously described species: A. acerosa s.str., A. glauca, A. latispora, A. subglobisemen, and Rhodocybe tillii (recombined as A. tillii). We described four new species: A. fenicola from Canadian prairie grasslands, A. juncorum and A. leucotricha, both on live and dead herbaceous material in European wetlands, and A. svalbardensis from the high Arctic. All nine taxa treated here were fixed with sequenced types. In addition, we identified seven other lineages, some only represented by a single collection, requiring further study before description, and four groups of two species or more, also requiring further dissection before circumscription of their constituents. The diversity of the complex with respect to size, colour, habitat, range, distribution, and substrate preference is made more intriguing by the presence of several lineages of brown omphalinoid species, differing from the typically pleurotoid forms in this complex. We generated 97 of the 131 ITS sequences studied, adding 65 new sequences from the acerosa complex.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.194
GPT teacher head0.299
Teacher spread0.105 · 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 designObservational
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

Citations1
Published2020
Admission routes1
Has abstractyes

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