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Record W2514805996 · doi:10.5539/jas.v8n9p23

Effect of Biochar Amendment on Bioavailability and Accumulation of Cadmium and Trace Elements in Brassica chinensis L. (Chinese Cabbage)

2016· article· en· W2514805996 on OpenAlexvenueno aff
Kiran Yasmin Khan, Barkat Ali, Xiaoqiang Cui, Ying Feng, Peter J. Stoffella, Fengshan Pan, Lin Tang, Xiaoe Yang

Bibliographic record

VenueJournal of Agricultural Science · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
FundersChina Scholarship Council
KeywordsBiocharAmendmentBioavailabilityCadmiumChemistryShootBrassicaCultivarAgronomyPhytotoxicityEnvironmental chemistryHorticultureBiology

Abstract

fetched live from OpenAlex

This study aims to investigate the potential use of maize stalk (MS), bamboo (BB) and cow manure (CM) derived biochar as a soil amendment on the cadmium (Cd) and trace metals (Mn, Zn, Fe) accumulation by two Cd accumulator cultivars; low (Aijiaoheiye 333) (AJ) and high (Zhouyeheiyoudonger) (ZH) of Brassica chinensis L. The effect of different biochar (4% w/w) on both cultivars grown on artificially Cd contaminated Alfisol soil was studied. All selected biochar decreased the bioavailability of Cd in soil and its phytoavailability for both cultivars of B. chinensis L. followed the order: MS > CM > BB. In particular, with increase soil pH MS biochar significantly reduced Cd bioavailability in soil by 54% and accumulation in shoots of AJ and ZH cultivars by 35% and 41%, as soil amendment. These results indicated the effectiveness of biochar by reducing the availability and phytotoxicity of Cd, while enhanced shoot dry biomass by promoting microbial activity and availability of essential trace metals.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.011
GPT teacher head0.286
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), 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

Citations23
Published2016
Admission routes1
Has abstractyes

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