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Record W4225277473 · doi:10.1515/bot-2022-0007

Three new species of <i>Asteromenia</i> (Hymenocladiaceae, Rhodophyta) from Australia

2022· article· en· W4225277473 on OpenAlexafffund
John M. Huisman, Gary W. Saunders

Bibliographic record

VenueBotanica Marina · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaNew Brunswick Innovation FoundationCanada Foundation for Innovation
KeywordsDNA barcodingTaxonomy (biology)BiologyTaxonZoologyGenusEcologyBotany

Abstract

fetched live from OpenAlex

Abstract Three new species of the red algal genus Asteromenia (Hymenocladiaceae, Rhodophyta) are described based on morphological and molecular analyses. DNA analyses of recent collections of topotype specimens of A. examinans from the Houtman Abrolhos Islands (Western Australia) have revealed that the specimens used in earlier studies were misidentified and represent an undescribed species, herein described as A. juliannae. Two additional new species, A. crenulata and A. praetermissa are described from the Cocos (Keeling) Islands, and Norfolk Island, respectively. For the most part, the new species showed morphological overlap and could not be discriminated based on morphology alone; DNA sequencing is therefore considered essential for accurate species recognition. The potential for misidentification of morphologically similar specimens highlights the need for sequence analyses and reference barcoding to be based on type or topotype specimens. The adoption of molecular methods in alpha taxonomy has led to the recognition of considerable diversity in the Rhodymeniales and it is envisaged that further collecting will add even more taxa to Australia’s rich algal flora.

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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.035
GPT teacher head0.211
Teacher spread0.176 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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