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Record W2581099241 · doi:10.1111/2041-210x.12736

paco: implementing Procrustean Approach to Cophylogeny in R

2017· article· en· W2581099241 on OpenAlexaff
Matthew C. Hutchinson, E. Fernando Cagua, Juan Antonio Balbuena, Daniel B. Stouffer, Timothée Poisot

Bibliographic record

VenueMethods in Ecology and Evolution · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversité de MontréalCégep Marie-Victorin
FundersRoyal Society Te ApārangiRoyal SocietyGeneralitat ValencianaUniversity of Canterbury
KeywordsPhylogenetic treeCongruence (geometry)Extant taxonSystematicsTaxonBiogeographyConcordanceEcologyEvolutionary biologyBiologyPhylogeneticsCoevolutionTaxonomy (biology)Psychology

Abstract

fetched live from OpenAlex

Summary The concordance of evolutionary histories and extant species interactions provides a useful metric for addressing questions of how the structure of ecological communities is influenced by macro‐evolutionary processes. We introduce paco (v0.3.1), an R package to perform Procrustean Approach to Cophylogeny. This method assesses the phylogenetic congruence, or evolutionary dependence, of two groups of interacting species using both ecological interaction networks and their phylogenetic history. We demonstrate the functionality of paco through its application to empirical host‐parasite and plant‐pollinator communities. Although the package is intended to assess the phylogenetic congruence between groups of interacting species, the method is also directly applicable to other scenarios that may show phylogenetic congruence including historical biogeography, molecular systematics, and cultural evolution.

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)0.014
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.080
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.049
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0040.003
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0060.005
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0800.041

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 head0.094
GPT teacher head0.346
Teacher spread0.252 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations167
Published2017
Admission routes1
Has abstractyes

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