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Record W3145670215 · doi:10.2307/3079146

Lower Species Richness in Dioecious Clades

2000· article· en· W3145670215 on OpenAlexaff
Heilbuth

Bibliographic record

VenueThe American Naturalist · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiologyDioecySpecies richnessPlant reproductive morphologyAllopatric speciationSister groupEcologyGenusTaxonCladePhylogenetic treePollenPopulationGeneticsDemography

Abstract

fetched live from OpenAlex

Despite the extensive research on the potential benefits of dioecy to individuals, little is known about the long‐term success of dioecious lineages in relation to their hermaphroditic or monoecious relatives. This study reports on the evolutionary success of worldwide dioecious flora in light of recent phylogenetic work by performing sister‐group comparisons of species richness between clades of angiosperms with different breeding systems. Whether this analysis is performed at the family or genus level, species richness is generally far lower in dioecious taxa when compared to their hermaphroditic or monoecious sister taxa. Despite the advantages of avoiding inbreeding depression and of allocating resources separately to male and female function, dioecy in angiosperms does not appear to be a key innovation promoting evolutionary radiation. A potential explanation for the low representation of dioecious lineages is that dioecious plants may have lower colonization rates. Baker’s Law states that self‐compatible lineages will have higher rates of successful long‐range dispersal since they do not require a mate; consequently, self‐compatible lineages may have higher rates of allopatric speciation. However, identical analyses performed with hermaphroditic self‐incompatible angiosperms did not produce similar results, suggesting that Baker’s law is not the reason for the poor representation of dioecy among angiosperm species.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.567
Threshold uncertainty score0.921

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)0.0010.000

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 head0.023
GPT teacher head0.213
Teacher spread0.191 · 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 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

Citations44
Published2000
Admission routes1
Has abstractyes

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