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Record W2975848926 · doi:10.1139/cjb-2019-0035

Comparative leaf anatomy of <i>Baccharis</i> (Asteraceae) from high-altitude grasslands in Brazil: taxonomic and ecological implications

2019· article· en· W2975848926 on OpenAlexvenueno aff
Thales Ornellas, Gustavo Heiden, Bruna Nunes de Luna, Cláudia Franca Barros

Bibliographic record

VenueBotany · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Diversity and Evolution
Canadian institutionsnot available
Fundersnot available
KeywordsTrichomeBaccharisBiologyAltitude (triangle)BotanyAsteraceaeGenusHabitatXylemEcologyEffects of high altitude on humansSpecies richnessAnatomy

Abstract

fetched live from OpenAlex

Baccharis L. is an important genus in the high-altitude environments of South America. In the State of Rio de Janeiro, southeastern Brazil, the greatest richness is found at montane and high-montane ecological refuges, known as high-altitude grasslands. The high altitude provides several environmental factors that turn the high-altitude grasslands into singular tropical habitats. In this article we describe the leaf anatomy of six shrubby species of Baccharis, pointing out ecological aspects of the structures, and testing the use of statistical analyses to compare the species. Using light and scanning electron microscopy, we compared the anatomical features of the leaves and performed a cluster and principal component analyses. The results show novel features in the leaf anatomy for the genus, such as an undulated cuticle, three secretory ducts in the midrib, secretory ducts associated with the xylem, and biseriate glandular trichomes with an irregular organization of the cells. Our statistical analyses indicate that the anatomical features of the leaves, especially trichomes, are useful for distinguishing the different species, whereas others, such as isobilateral mesophyll, amphistomatic leaves, and an epidermis with thick cell walls correlate with their high-elevation habitat.

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 score0.186
Threshold uncertainty score0.287

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 head0.018
GPT teacher head0.234
Teacher spread0.215 · 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

Citations15
Published2019
Admission routes1
Has abstractyes

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