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Record W3134057599 · doi:10.3390/agriculture11030205

Growth Responses and Accumulation Characteristics of Three Ornamental Plants to Sn Contamination in Soil

2021· article· en· W3134057599 on OpenAlexaff
Yuxia Liu, Weili Xu, Yi Wang, Weiduo Hao, Qixing Zhou, Jianv Liu

Bibliographic record

VenueAgriculture · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsImpatiensOrnamental plantTagetesPhytoremediationHyperaccumulatorBotanyBioconcentrationBiologyBalsaminaceaeHorticultureContaminationCultivarEcology

Abstract

fetched live from OpenAlex

Decorative ornamental plants have been applied as hyperaccumulators/phytoremediators to a wide spectrum of heavy metal contaminants. In this study, pot culture experiments were conducted to investigate the Sn tolerance and accumulation in Impatiens balsamina L., Mirabilis jalapa L. and Tagetes erecta L., in order to assess the possibility of these three ornamental plants to be used as phytoremediators of Sn-contaminated soil. Results show that all three plants exhibited strong tolerance to Sn contamination, and no significant visual toxicity was observed for all three plants grown under most of the Sn treatments. The amount of Sn accumulated in the three plants was positively correlated with the Sn concentration in the soil. The order of the Sn accumulative capacity was Impatiens balsamina > Mirabilis jalapa > Tagetes erecta. Impatiens balsamina and Tagetes erecta showed a low translocation ability (TF) (<1), and the roots accumulated the highest Sn concentration, but Impatiens balsamina showed a relatively high bioconcentration factor (BCF, Sn concentration in each part > 100 mg/kg after Sn treatment of 500 mg/kg). Meanwhile, the TF of Mirabilis jalapa was >1, and the fluorescence accumulated the most Sn. In combination with the adaptation to high concentrations of various heavy metals, these three ornamental plants are potential candidates for Sn mining tailings or contaminated soil.

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 score0.266
Threshold uncertainty score0.267

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 head0.242
Teacher spread0.225 · 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

Citations20
Published2021
Admission routes1
Has abstractyes

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