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Record W2788530561 · doi:10.1139/cjb-2017-0185

Quantitative developmental analysis of two phenotypes of <i>Hibiscus rosa-sinensis</i> in the context of homeosis

2018· article· en· W2788530561 on OpenAlexaffvenue
Christian H. Norton, Christian Lacroix

Bibliographic record

VenueBotany · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Molecular Biology Research
Canadian institutionsUniversity of Prince Edward Island
FundersDartmouth College
KeywordsPrimordiumStamenBiologyPetalSepalGynoeciumBotanyWhorl (mollusc)GeneticsPollenGene

Abstract

fetched live from OpenAlex

The flowers of Hibiscus rosa-sinensis L. (Malvaceae) exist in two floral morphologies: a single phenotype, and a double phenotype. This study focused on the early stages of floral development, just before the initiation of petal primordia and up until the bifurcation of the stamen primordia. The two phenotypes were compared using logistic regression and bootstrapping techniques. Four aspects of floral development were considered: (i) organogenesis of petal and stamen primordia, and stamen bifurcation; (ii) allometry of stamen primordia; (iii) morphology of stamen primordia; and (iv) size of stamen primordia. The single and double buds initiated petal primordia at the same bud radii, but double buds initiated stamen primordia and stamen bifurcation at larger bud radii than the single phenotype. Double stamen primordia were shorter, wider, and more spherical than single stamen primordia, although the sizes of the single and double stamen primordia (defined as the sum of their length and width measurements) were not different. Results suggest that the additional space on the floral meristem of the double phenotype is linked to the divergent development of stamen primordia occupying this extra space.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.034
GPT teacher head0.284
Teacher spread0.250 · 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
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

Citations0
Published2018
Admission routes2
Has abstractyes

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