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Record W6967043445 · doi:10.5061/dryad.4r1k05t

Data from: Evolution of invasiveness by genetic accommodation

2018· dataset· en· W6967043445 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2018
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicSunflower and Safflower Cultivation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAdaptation (eye)Adaptive evolutionPhenotypeGenetic variationInvasive speciesSelection (genetic algorithm)Sunflower

Abstract

fetched live from OpenAlex

Invasion success of species introduced to novel environments may be facilitated by adaptive evolution and by phenotypic plasticity. Here we investigate the independent and joint contribution of both mechanisms as drivers of invasiveness in the perennial sunflower Helianthus tuberosus. We show that invasive genotypes have multiple origins, and that invasive spread was facilitated by the repeated evolution of extreme values in a single trait, clonality. In line with genetic accommodation theory, we establish that this evolutionary transition occurred by refining a preexisting plastic response of clonality to water availability. Further, we demonstrate that under the non-drought conditions typically experienced by this plant in its introduced range, invasive spread is mediated by hybrid vigor and/or two major additive-effect loci, and that these mechanisms are complementary. Thus, in H. tuberosus, evolution of invasiveness was facilitated by phenotypic plasticity, and involved the use of multiple genetic solutions to achieve the same invasiveness trait.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.040
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0400.027

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 head0.033
GPT teacher head0.247
Teacher spread0.214 · 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 designNot applicable
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
Published2018
Admission routes1
Has abstractyes

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