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Record W2180114857 · doi:10.1139/b2012-046

Leaf epidermal characters of Brazilian species of <i>Solanum</i> section <i>Torva</i> as taxonomic evidence

2012· article· en· W2180114857 on OpenAlexvenueno aff
Kiriaki Nurit‐Silva, Rafael Costa Silva, Ionaldo José Lima Diniz Basílio, Maria de Fátima Agra

Bibliographic record

VenueBotany · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Diversity and Evolution
Canadian institutionsnot available
Fundersnot available
KeywordsSolanumTrichomeBiologyBotanySolanaceaeEpidermis (zoology)Anatomy

Abstract

fetched live from OpenAlex

To evaluate their taxonomic significance, the yet unknown epidermal characters of the leaves of 10 species of Solanum section Torva Nees (Solanaceae) from Brazil were determined using light and scanning electron microscopy. Our results indicated that the leaf epidermis showed epidermal cells of straight to wavy outlines. The curved pattern of anticlinal cell walls was predominant on the adaxial surface, and the wave pattern was predominant on the abaxial surface. Leaves are amphistomatic in six species ( Solanum acutilobum Dunal, Solanum bonariense L., Solanum guaraniticum A. St.-Hil., Solanum metrobotryon Dunal, Solanum scuticum M. Nee, and Solanum torvum Sw.) and hypostomatic in four species ( Solanum adspersum Witasek, Solanum paniculatum L., Solanum subumbellatum Vell., and Solanum variabile Mart.). Four different types of stomata (anisocytic, anomocytic, paracytic, and diacytic) were recognized, with the anisocytic type predominant on adaxial and abaxial surfaces. The combination of some micro-morphological characters such as density, distribution and type of stellate trichomes, the anticlinal walls of epidermal cells, and also the type and distribution of stomata proved to be the most useful and distinctive characters for the separation of species. This set of characters also may help as additional support in quality control of species having medicinal importance.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score0.271

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 head0.039
GPT teacher head0.209
Teacher spread0.170 · 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

Citations14
Published2012
Admission routes1
Has abstractyes

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