MétaCan
Menu
Back to cohort
Record W2614970618 · doi:10.3390/ijerph14050532

Influence of Nano-Hydroxyapatite on the Metal Bioavailability, Plant Metal Accumulation and Root Exudates of Ryegrass for Phytoremediation in Lead-Polluted Soil

2017· article· en· W2614970618 on OpenAlexaff
Ling Ding, Jianbing Li, Wei Liu, Qingqing Zuo, Shuxuan Liang

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsPhytoremediationEnvironmental remediationBioavailabilityShootChemistryEnvironmental chemistryCadmiumSoil contaminationMetalSoil remediationSoil waterAgronomyContaminationHorticultureHeavy metalsBiologyEnvironmental scienceSoil scienceEcology

Abstract

fetched live from OpenAlex

Lead is recognized as one of the most widespread toxic metal contaminants and pervasive environmental health concerns in the environment. In this paper, the effects of nano-hydroxyapatite (NHAP) on remediation in artificially Pb-contaminated soils and ryegrass were studied in a pot experiment. The addition of NHAP decreased the water- and acid-soluble, exchangeable, and reducible fractions of Pb, extracted using the Community Bureau of Reference (BCR) method, whilst greatly increasing the residual fraction of Pb. Oxidizable Pb was increased slightly. No significant increase in soil pH was caused by the application of NHAP. Compared to conditions without NHAP, the addition of NHAP decreased the Pb content in ryegrass shoots and roots by 13.19-20.3% and 2.86-21.1%, respectively. Therefore, the application of NHAP reduced the mobility and bioavailability of Pb in the soil. In addition, the application of NHAP improved the fresh weight of shoots and roots, and promoted the growth of ryegrass. NHAP played a positive role in stimulating ryegrass to secrete tartaric acid.

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)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.117
GPT teacher head0.396
Teacher spread0.279 · 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

Citations72
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Environmental Research and Public HealthSame topicHeavy metals in environmentFrench-language works237,207