MétaCan
Menu
Back to cohort
Record W4291019109 · doi:10.1038/s42003-022-03771-5

Geographic isolation drives speciation in Nearctic aphids

2022· article· en· W4291019109 on OpenAlexafffund
Nate B. Hardy, Chloe Kaczvinsky, Gwendolyn Bird, Robin Richter, Jeremy R. Dettman, Eric Maw, Bryan M. T. Brunet, Robert G. Foottit

Bibliographic record

VenueCommunications Biology · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaGovernment of CanadaNational Science Foundation
KeywordsSpecies richnessBiologyGenetic algorithmNicheAphidEcologyCladeEvolutionary biologyHost (biology)Ecological speciationDivergence (linguistics)Species diversityPhylogenetic treeGenetic variationGene flowBotanyGenetics

Abstract

fetched live from OpenAlex

Across herbivorous insect clades, species richness and host-use diversity tend to positively covary. This could be because host-use divergence drives speciation, or because it raises the ecological limits on species richness. To evaluate these hypotheses, we performed phylogenetic path model analyses of the species diversity of Nearctic aphids. Here, we show that variation in the species richness of aphid clades is caused mainly by host-use divergence, whereas variation in speciation rates is caused more by divergence in non-host-related niche variables. Aphid speciation is affected by both the evolution of host and non-host-related niche components, but the former is largely caused by the latter. Thus, our analyses suggest that host-use divergence can both raise the ecological limits on species richness and drive speciation, although in the latter case, host-use divergence tends to be a step along the causal path leading from non-host-related niche evolution to speciation.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.071
GPT teacher head0.254
Teacher spread0.184 · 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

Citations10
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueCommunications BiologySame topicPlant and animal studiesFrench-language works237,207