MétaCan
Menu
Back to cohort
Record W6911239532 · doi:10.5061/dryad.hx3ffbgnm

Investigating historical drivers of latitudinal gradients in polyploid plant biogeography: A multi-clade perspective

2024· dataset· en· W6911239532 on OpenAlexaff

Bibliographic record

VenueDRYAD · 2024
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPolyploidLatitudePloidyTaxonNichePerspective (graphical)Ecological niche

Abstract

fetched live from OpenAlex

Premise of the Study The proportion of polyploid plants in a community increases with latitude, and different hypotheses have been proposed about which factors drive this pattern. Here, we aim to understand the historical causes of the latitudinal polyploidy gradient using a combination of ancestral state reconstruction methods. Specifically, we assess whether (1) polyploidization enables movement to higher latitudes (i.e., polyploidization precedes occurrences in higher latitudes) or (2) higher latitudes facilitate polyploidization (i.e., occurrence in higher latitudes precedes polyploidization). Methods We reconstruct the ploidy states and ancestral niches of 1,032 angiosperm species at four paleoclimatic time slices ranging from 3.3 million years ago to the present, comprising taxa from four well-represented clades: Onagraceae, Primulaceae, Solanum (Solanaceae), and Pooideae (Poaceae). We use ancestral niche reconstruction models alongside a customized discrete character evolution model to allow reconstruction of states at specific time slices. Patterns of latitudinal movement are reconstructed and compared in relation to inferred ploidy shifts. Key Results We find that no single hypothesis applies equally well across all analyzed clades. While significant differences in median latitudinal occurrence were detected in the largest clade, Poaceae, no significant differences were detected in latitudinal movement in any clade. Conclusions Our preliminary study is the first to attempt to connect ploidy changes to continuous latitudinal movement, but we cannot favor one hypothesis over another. Given that patterns seem to be clade-specific, a larger number of clades must be analyzed in future studies for generalities to be drawn.

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)0.002
metaresearch head score (Gemma)0.003
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: Dataset · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.036
GPT teacher head0.288
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 designObservational
Domainnot available
GenreDataset

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 venueDRYADFrench-language works237,207