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Record W2561488935 · doi:10.1071/sb16025

Dating the emergence of truffle-like fungi in Australia, by using an augmented meta-analysis

2016· article· en· W2561488935 on OpenAlexaff
Elizabeth M. Sheedy, Martin Ryberg, Teresa Lebel, Tom W. May, Neale L. Bougher, P. Brandon Matheny

Bibliographic record

VenueAustralian Systematic Botany · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsDepartment of Environment and Conservation
FundersNational Science Foundation
KeywordsAridificationBiologyEcologyArid

Abstract

fetched live from OpenAlex

Australia supports a high diversity of sequestrate (truffle-like) macrofungi. This has long been thought to be related to the predominantly or seasonally dry climate. The present study posits that if aridity were a key factor in the evolution of sequestrate fruit-bodies, most sequestrate species would have emerged in Australia only after it began to aridify, which occurred post-separation with Antarctica (c. 32 million years ago). Focusing on the high phylogenetic diversity of sequestrate taxa in the Agaricomycetes in Australia, dates of sequestrate nodes were compiled directly from published phylogenies (four lineages) or created using sequences available on GenBank that were processed in BEAST using a secondary calibration method (nine lineages). Although the morphologically diverse Hysterangiales was found to be the first group to become sequestrate, c. 83 million years ago, overall sequestration in Australia occurred more recently. Models were created and compared and support was found for an increased rate of sequestration in Australia at some point between 34 and 13 million years ago (during the Oligocene and Miocene). Although the rate of sequestration is shown to have increased in Australia after separation from Antarctica, the timing also overlaps with the radiation of potential mycorrhizal plant associates, and the emergence of specialised mycophagous marsupials. Although aridification is evidently not the sole driver of sequestration, it is still likely to have had a major influence on the diversity of sequestrate fungi in Australia. Comparisons with other regions of high sequestrate diversity will be informative.

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)0.004
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: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0050.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.126
GPT teacher head0.304
Teacher spread0.178 · 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 designMeta-analysis
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

Citations15
Published2016
Admission routes1
Has abstractyes

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