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Record W4309625182 · doi:10.1038/s41597-022-01812-6

An expert curated global legume checklist improves the accuracy of occurrence, biodiversity and taxonomic data

2022· article· en· W4309625182 on OpenAlexafffund
M. Marianne le Roux, Joseph T. Miller, John C. Waller, Markus Döring, Anne Bruneau

Bibliographic record

VenueScientific Data · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant and Fungal Species Descriptions
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaUniversité de MontréalRoyal Botanical Gardens, Kew
KeywordsChecklistTaxonTaxonomic rankBiodiversityBiologyGlobal biodiversityEcology

Abstract

fetched live from OpenAlex

The Legume Phylogeny Working Group's Taxonomy Working Group was tasked to create a community endorsed global legume checklist that will serve as a primary source of taxa for biodiversity data platforms and legume-related research. The checklist was published in June 2021, recognising 772 genera and 22,360 species. It is disseminated through the new Legume Data Portal as part of the Global Biodiversity Information Facility (GBIF) hosted portal initiative. The process that was followed to publish and disseminate the checklist and its content is described here. The impact of the work by the Taxonomy Working Group are quantified by comparing the published checklist with the GBIF taxonomic backbone. A total of 44,157 names overlapped with the GBIF taxonomic backbone while 30,456 names were added, which enabled more accurate name matching of 61,235 legume occurrences. Continuous improvement to the World Checklist of Vascular Plants (WCVP): Fabaceae checklist will allow the GBIF taxonomic backbone and other checklist managers to converge to a consistent and comprehensive list of legume taxa globally over time.

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0030.004
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 head0.065
GPT teacher head0.296
Teacher spread0.231 · 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 designNot applicable
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

Citations8
Published2022
Admission routes2
Has abstractyes

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