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THE ROLE OF DIVERSIFICATION IN CAUSING THE CORRELATES OF DIOECY

2004· article· en· W2178501268 on OpenAlexaff
Jana C. Vamosi, Steven M. Vamosi

Bibliographic record

VenueEvolution · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of CalgaryUniversity of Toronto
Fundersnot available
KeywordsBiologyDioecySpecies richnessEcologySister groupEvolutionary biologyPhylogeneticsCladeGeneticsGene

Abstract

fetched live from OpenAlex

Dioecy is reported to be correlated with a number of ecological traits, including tropical distribution, woody growth form, plain flowers, and fleshy fruits. Previous analyses have concentrated on determining whether dioecy is more likely to evolve in lineages possessing these traits, rather than considering the speciation and extinction rates of dioecious lineages with certain combinations of traits. To address the association between species richness in dioecious lineages as a function of the ecological traits, we compared the evolutionary success (i.e., relative species richness) of dioecious focal lineages with that of their nondioecious sister groups. This test was repeated for the evolutionary success of randomly chosen nondioecious lineages (control lineages) compared with their nondioecious sister groups. If the possession of certain ecological traits enhances the evolutionary success of dioecious lineages, we predict an association between the presence of these traits and relative species richness in the former, but not latter, set of sister-group comparisons. Dioecious focal lineages with a higher number of these traits experienced higher evolutionary success in sister-group comparisons, whereas no trend was found for the control focal lineages. The increase in evolutionary success was especially true for dioecious focal lineages that had a tropical distribution or fleshy fruit. We discuss how these results provide strong support for differential evolutionary success theories for the correlations between dioecy and the ecological traits considered.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
Threshold uncertainty score0.146

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 head0.019
GPT teacher head0.182
Teacher spread0.163 · 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

Citations82
Published2004
Admission routes1
Has abstractyes

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