MétaCan
Menu
Back to cohort
Record W4404018856 · doi:10.1101/2024.11.03.621532

The distribution of clade size in a coalescent diversification model

2024· preprint· en· W4404018856 on OpenAlexaff
Yexuan Song, Caroline Colijn, Ailene MacPherson

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicEvolution and Paleontology Studies
Canadian institutionsToronto Metropolitan UniversitySimon Fraser University
Fundersnot available
KeywordsCoalescent theoryDiversification (marketing strategy)CladeDistribution (mathematics)GeographyEvolutionary biologyEconometricsBiologyMathematicsBusinessPhylogeneticsGenetics

Abstract

fetched live from OpenAlex

Abstract Characterizing the patterns and determinants of biological diversity is a central aim of evolutionary biology. Doing so requires developing expectations for clade size, the relationship between the number of species in a clade and its age, for standard diversification models, such as the coalescent or birth-death process. These expectations are necessary for identifying diversity outliers, specious or depauperate clades in the macroevolutionary context or transmission clusters of particular or little public health concern in the epidemiological context, and for testing alternative diversification hypotheses. Here, we derive a closed-form expression for the distribution of clade sizes under the widely used Kingman coalescent diversification model and extend the results to allow the number of niches (the effective population size) to vary through time. This result complements analogous results for the birth-death model in that it provides expectations for the joint distribution of clade sizes for a case where diversification is strongly density-dependent.

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)0.008
metaresearch head score (Gemma)0.026
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.003
Scholarly communication0.0030.004
Open science0.0040.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.018
GPT teacher head0.213
Teacher spread0.195 · 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
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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)Same topicEvolution and Paleontology StudiesFrench-language works237,207