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Record W4206985141 · doi:10.5539/jps.v11n1p10

Adaptative Leaf Morphology of Eurya japonica Thunb. (Ternstroemiaceae) in Serpentine Areas

2022· article· en· W4206985141 on OpenAlexvenueno aff
Masayuki Shiba, Tomoki Tate, Tatsuya Fukuda

Bibliographic record

VenueJournal of Plant Studies · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFern and Epiphyte Biology
Canadian institutionsnot available
Fundersnot available
KeywordsJaponicaMorphology (biology)BiologySpongy tissueBotanyThickeningSoil waterNutrientEpidermis (zoology)Leaf sizeHorticulturePalisade cellAnatomyEcologyChemistryZoology

Abstract

fetched live from OpenAlex

Serpentine soils are known to affect plant growth and plants in these soils have morphological and anatomical modifications. Comparative leaf morphology and anatomy studies of Eurya japonica Thunb. was conducted between serpentine and inland (control) areas. Our morphological analyses revealed that the individuals in the serpentine areas had significantly smaller and thicker leaves than those in the inland areas. Our anatomical analyses showed that the smaller leaves of serpentines had decreased numbers of cells, and their thicker leaves contributed to the increased height of epidermal cells, palisade tissue, and spongy tissue. Furthermore, the stomatal size of serpentines was significantly smaller than those from the inland areas. We concluded that E. japonica adapted to the serpentine areas by decreasing leaf size due to low levels of nutrients, by thickening the leaves to store water and reducing the stomatal size to minimize water loss via gas exchange.

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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.047
GPT teacher head0.260
Teacher spread0.213 · 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

Citations12
Published2022
Admission routes1
Has abstractyes

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