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Record W2960467764 · doi:10.22621/cfn.v132i4.1997

Diversity and conservation status of lichens and allied fungi in the Greater Toronto Area: results from four years of the Ontario BioBlitz

2019· article· en· W2960467764 on OpenAlexafffundvenueabout
R. Troy McMullin, Katherine H. I. Drotos, D. G. Ireland, Hanna Dorval

Bibliographic record

VenueThe Canadian Field-Naturalist · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLichen and fungal ecology
Canadian institutionsRoyal Ontario MuseumUniversity of GuelphCanadian Museum of Nature
FundersNature Conservancy of CanadaUniversity of TorontoCanadian Wildlife FederationParks CanadaBird Studies Canada
KeywordsLichenGeographyTaxonEcologyBiodiversityBiology

Abstract

fetched live from OpenAlex

Bioblitzes are typically 24-hour biological surveys of a defined region carried out by taxonomic specialists, citizen scientists, and the general public. The largest in Canada is the Ontario BioBlitz, an annual event held in the Greater Toronto Area (GTA). Between 2013 and 2016, we examined the feasibility of including lichens and allied fungi in the Ontario BioBlitz. These taxa are often overlooked, understudied, and taxonomically difficult. We completed a bioblitz in each of the four major watersheds in the GTA and recorded 138 species in 72 genera which, combined with all previous collections, totals 180 species in 88 genera in the area. Thirteen of the species we collected are provincially ranked as S1 (critically imperilled), S2 (imperilled), or S3 (vulnerable). We collected Lecanora carpinea for the first time in Ontario. Our results provide a baseline list of GTA lichens that can be used for monitoring. This is one of the first detailed lichen surveys of a major North American urban area and it demonstrates that rapid bioblitz surveys are proficient in capturing lichen diversity despite their inconspicuous nature and the advanced microscopy and chemical analyses required for their identification.

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 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 score0.014
Threshold uncertainty score0.253

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 science0.0000.000
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 head0.029
GPT teacher head0.190
Teacher spread0.162 · 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 teacher head, 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

Citations7
Published2019
Admission routes4
Has abstractyes

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