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Induced plant uptake and transport of mercury in the presence of sulphur‐containing ligands and humic acid

2005· article· en· W2145667027 on OpenAlexaff
Fábio Netto Moreno, Christopher W. N. Anderson, Robert B. Stewart, Brett Robinson, Mory Ghomshei, J.A. Meech

Bibliographic record

VenueNew Phytologist · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of British Columbia
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsShootChemistryPhaseolusBrassicaMercury (programming language)AmmoniumSulfurHumic acidBotanySulfateEnvironmental chemistryHorticultureBiologyFertilizer

Abstract

fetched live from OpenAlex

The induced accumulation of mercury (Hg) by plants was investigated for the species Phaseolus vulgaris (Bush bean), Brassica juncea (Indian mustard), and Vicia villosa (Hairy vetch). All plants were grown in modified Hg-contaminated mine tailings and were treated with sulphur-containing ligands to induce Hg accumulation. The effects of varied substrate Hg concentration and humic acid (HA) level on the induced plant-Hg accumulation for B. juncea were examined. Thiosulphate salts (ammonium and sodium) mobilised Hg in the substrates and caused an increase in the Hg concentration of roots and shoots of all tested plant species. Root Hg accumulation was positively correlated to extractable Hg for (NH4)2S2O3-treated B. juncea plants grown in HA-amended substrates. However, shoot Hg translocation for this species was inhibited at 1.25 g HA kg(-1) of substrate. Mercury-thiosulphate complexes could be translocated and accumulated in the upper parts of the plants up to 25 times the Hg concentration in the substrate. We conclude that shoot Hg accumulation in the presence of thiosulphate salts is dependent upon plant species characteristics (e.g. root surface area) and humic acid content.

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 score0.116
Threshold uncertainty score0.215

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 head0.036
GPT teacher head0.275
Teacher spread0.239 · 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

Citations102
Published2005
Admission routes1
Has abstractyes

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