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Record W2076860346 · doi:10.1080/15226514.2012.723064

Phosphorus Improves Arsenic Phytoremediation by<i>Anadenanthera Peregrina</i>by Alleviating Induced Oxidative Stress

2012· article· en· W2076860346 on OpenAlexaff
Marcelo Pedrosa Gomes, Maria Ângela Machado de Carvalho, Geila Santos Carvalho, Thyago Carvalho Marques, Queila Souza Garcia, Luiz Roberto Guimarães Guilherme, Â. M. Soares

Bibliographic record

VenueInternational Journal of Phytoremediation · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsArsenateArsenicPhytoremediationChemistryCatalasePhosphorusLipid peroxidationPhytotoxicityPhosphateOxidative stressArsenic toxicityHydrogen peroxideReactive oxygen speciesBotanyPeroxidaseHorticultureEnvironmental chemistryFood scienceBiochemistryBiologyEnzyme

Abstract

fetched live from OpenAlex

Due to similarities in their chemical behaviors, studies examining interactions between arsenic (As)--in special arsenate--and phosphorus (P) are important for better understanding arsenate uptake, toxicity, and accumulation in plants. We evaluated the effects of phosphate addition on plant biomass and on arsenate and phosphate uptake by Anadenanthera peregrina, an important Brazilian savanna legume. Plants were grown for 35 days in substrates that received combinations of 0, 10, 50, and 100 mg kg(-1) arsenate and 0, 200, and 400 mg kg(-1) phosphate. The addition of P increased the arsenic-phytoremediation capacity of A. peregrina by increasing As accumulation, while also alleviating As-induced oxidative stress. Arsenate phytotoxicity in A. peregrina is due to lipid peroxidation, but not hydrogen peroxide accumulation. Added P also increased the activity of important reactive oxygen species-scavenging enzymes (catalase and ascorbate peroxidase) that help prevent lipid peroxidation in leaves. Our findings suggest that applying P represents a feasible strategy for more efficient As phytoremediation using A. peregrina.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.008
GPT teacher head0.242
Teacher spread0.235 · 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

Citations54
Published2012
Admission routes1
Has abstractyes

Explore more

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