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Record W2956411513 · doi:10.1139/gen-2019-0069

Relative genome size variation in the African agroforestry tree <i>Parkia biglobosa</i> (Fabaceae: Caesalpinioideae) and its relation to geography, population genetics, and morphology

2019· article· en· W2956411513 on OpenAlexvenueno aff
Christoph Dobeś, Irene Steccari, Selina Köstenberger, Djingdia Lompo

Bibliographic record

VenueGenome · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAfrican Botany and Ecology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyCaesalpinioideaeGenome sizeEvolutionary biologyGenetic variationPopulationFabaceaeParkia biglobosaIntraspecific competitionGenomeZoologyEcologyGeneticsGene

Abstract

fetched live from OpenAlex

Variation in genome size and in chromosome number can be linked to genetic, morphological, and ecological characteristics, and thus be taxonomically significant. We screened the relative genome size (RGS) and counted the number of mitotic chromosomes in the African agroforestry tree Parkia biglobosa, a widely distributed savannah species that shows conspicuous morphological clinal variation and strong genetic structure, and tested for linkage of RGS variation to geography, leaf morphology, and population genetic variation. An improved protocol for the preparation of chromosomes was developed. The study is based on 58 individuals from 15 populations covering most of the distribution range of the species. We observed differences in RGS among individuals of up to 10.2%, with some of the individuals differing statistically in RGS from the bulk of screened individuals. Most of the RGS variation was within populations, whereas variation was unrelated to any of the tested features of the species. Those chromosome numbers that could be exactly established were invariable 2n = 2x = 26. In conclusion, there was no evidence from the karyological data for structured intraspecific taxonomic heterogeneity.

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 score0.164
Threshold uncertainty score0.273

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 head0.011
GPT teacher head0.200
Teacher spread0.189 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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