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Record W2098069303 · doi:10.1139/cjb-2013-0298

Comparative analysis of aluminum accumulation in leaves of three angiosperm species

2014· article· en· W2098069303 on OpenAlexvenueno aff
Eriko Maejima, Syuntaro Hiradate, Steven Jansen, Mitsuru Osaki, Toshihiro Watanabe

Bibliographic record

VenueBotany · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAluminum toxicity and tolerance in plants and animals
Canadian institutionsnot available
Fundersnot available
KeywordsMelastomataceaeBotanyBiologyTrichomeOxalateChemistry

Abstract

fetched live from OpenAlex

Aluminum (Al) accumulators are widely distributed in the plant kingdom but phylogenetic implications of internal Al detoxification mechanisms are not well understood. We investigated differences in the characteristics of Al accumulation (i.e., accumulation potential, chemical form, and localization) in three woody Al accumulators, Symplocos chinensis (Lour.) Druce var. leucocarpa (Nakai) Ohwi f. pilosa (Nakai) Ohwi (Symplocaceae, Ericales), Melastoma malabathricum L., and Tibouchina urvilleana Cogn. (both Melastomataceae, Myrtales). The order of Al accumulation potential under hydroponic conditions was S. chinensis ≈ M. malabathricum > T. urvilleana. Oxalate was at least partly involved in the internal Al detoxification mechanisms in leaves of all three Al accumulators, based on a correlation analysis between Al and organic acid in water and 0.02 mol·L −1 HCl extracts and the 27 Al nuclear magnetic resonance spectra of intact leaves. However, the Al forms in the leaves were not simple Al-ligand complexes in a specific cell structure. Al localization in leaf sections differed among the three species. Extremely high levels of Al were found in trichomes of the lower epidermis in leaves of T. urvilleana. These data illustrate that woody Al-accumulating angiosperms have independently developed various internal Al-tolerance mechanisms in which oxalate plays a significant role.

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 score0.427
Threshold uncertainty score0.364

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 head0.070
GPT teacher head0.277
Teacher spread0.207 · 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

Citations13
Published2014
Admission routes1
Has abstractyes

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