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Record W2550882012 · doi:10.1139/gen-2016-0071

DNA barcoding the bees (Hymenoptera: Apoidea) of Chile: species discovery in a reasonably well known bee fauna with the description of a new species of <i>Lonchopria</i> (Colletidae)

2016· article· en· W2550882012 on OpenAlexaffvenue
Laurence Packer, Luisa Ruz

Bibliographic record

VenueGenome · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsYork University
Fundersnot available
KeywordsDNA barcodingBiologyFaunaTaxonomy (biology)HymenopteraBarcodeZoologySpecies complexEvolutionary biologyApoideaEcology

Abstract

fetched live from OpenAlex

We compare the diversity of bees in the Chilean fauna as understood from traditional taxonomy-based catalogues with that currently known from DNA barcodes using the BIN system informed by ongoing morphology-based taxonomic research. While DNA barcode surveys of the Chilean bee fauna remain incomplete, it is clear that new species can readily be distinguished using this method and that morphological differentiation of distinct barcode clusters is sometimes very easy. We assess the situation in two genera in some detail. In Lonchopria Vachal one “species” is readily separable into two BINs that are easily differentiated based upon male mandibular and genitalic morphology (characters generally used in this group) as well as female hair patterns. Consequently, we describe Lonchopria (Lonchopria) heberti Packer and Ruz, new species. For Liphanthus Reed, a large number of new species has been detected using DNA barcoding and considerable additional traditional morphological work will be required to describe them. When we add the number of BINs (whether identified to named species or not) to the number of Chilean bee species that we know have not been barcoded (both described and new species under study in our laboratories) we conclude that the bee fauna of Chile is substantially greater than the 436 species currently known. Spanish language abstract available as supplementary data 1 .

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.958
Threshold uncertainty score0.201

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 head0.030
GPT teacher head0.180
Teacher spread0.150 · 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 designBench or experimental
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

Citations35
Published2016
Admission routes2
Has abstractyes

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