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Record W4388772842 · doi:10.1186/s13104-023-06609-3

The single and interactive effects of aluminium and low pH, or Ca/Al ratios on red pine seedlings

2023· article· en· W4388772842 on OpenAlexaff
Yunfeng Shan, Ammara Riaz

Bibliographic record

VenueBMC Research Notes · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAluminum toxicity and tolerance in plants and animals
Canadian institutionsUniversity of Toronto
FundersTokyo University of Agriculture and TechnologyTokyo University of Agriculture
KeywordsPinus densifloraPhytotoxicityAcid depositionAluminiumElongationChemistryHorticultureToxicityRed pineMolar ratioDry weightPinus <genus>BotanyNuclear chemistryAnimal scienceBiologyMaterials scienceMetallurgyBiochemistryEcology

Abstract

fetched live from OpenAlex

Acid deposition has been one of the major environmental pollution problems for longer than 50 years, since the 1970s. Investigation on the interactive effects of aluminum (Al) and low pH or Ca/Al ratios on red pine (Pinus densiflora Sieb. and Zucc.) has been required but lacking. In the present study, needles of red pine seedlings exposed to Al treatments with solution pH 4.0 and 3.5 exhibited purplish leaf characteristics of Al toxicity. The dry weights of the needle and whole plant, and the current needle elongation were linearly reduced with Al concentrations from 0, 13 to 26 ppm. Results show that red pine is an intermediate species in sensitivity to Al and is insensitive to low pH. However, the synergistic interactions of low pH treatments with the elevated Al were significant. Al toxicity to red pine was significantly enlarged with reduced pH. The root length and whole plant length were significantly decreased at 1:10 of Ca/Al ratios (p < 0.05), but Al phytotoxicity was completely lost when the Ca/Al molar ratio was 10:1. Liming is still an applicable measure to remediate acidification problems by natural or anthropogenic factors such as acid deposition.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.660
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.102
GPT teacher head0.344
Teacher spread0.242 · 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 designBench or experimental
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
Published2023
Admission routes1
Has abstractyes

Explore more

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