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Record W2507165062 · doi:10.1139/cjb-2016-0172

Secretory duct distribution and leaf venation patterns of <i>Aldama</i> species (Asteraceae) and their application in taxonomy

2016· article· en· W2507165062 on OpenAlexvenueno aff
Arinawa Liz Filartiga, Vanessa Bassinello, Gustavo Mortean Filippi, Aline Bertolosi Bombo, Beatriz Appezzato‐da‐Glória

Bibliographic record

VenueBotany · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Diversity and Evolution
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyBotanyAsteraceaeDuct (anatomy)Taxonomy (biology)TrichomeAnatomy

Abstract

fetched live from OpenAlex

Features of leaf morphology such as secretory duct distribution and venation patterns are important taxonomical tools; however, some species can have variation in these traits. This study evaluates whether the secretory duct distribution in the midrib and venation is similar across different leaves of 17 Aldama species. Six fully expanded leaves (three each of the largest and smallest size) from five distinct plants were selected to analyze the duct distribution. The samples were histologically examined, and the quantitative data were statistically analyzed. The venation pattern was analyzed in five fully expanded leaves of different plants. In all, 23 secretory duct distribution patterns were identified; they showed intra- and inter-species variations except in Aldama anchusifolia (DC.) E.E.Schill. &amp; Panero and A. trichophylla (Dusén) Magenta. The largest number of ducts was not correlated with leaf and midrib dimensions (width and length). Further, Aldama venation could be divided into two groups: (1) pinnate camptodromous brochidodromous type (four species), and (2) acrodromous venation type and its basal and suprabasal variations (13 species). Thus, distinct secretory duct arrangements of the midrib might assist in the discrimination of Aldama species. The venation patterns were also important for distinguishing the majority of species selected.

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 score0.591
Threshold uncertainty score0.068

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 head0.017
GPT teacher head0.167
Teacher spread0.150 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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