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Record W2162626300 · doi:10.4141/s01-072

Bioavailability of heavy metals in soils amended with sewage sludge

2002· article· en· W2162626300 on OpenAlexvenueno aff
M T Morera, Jesús C. Echeverría, Julián J. Garrido

Bibliographic record

VenueCanadian Journal of Soil Science · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
FundersInstituto Nacional de Investigación y Tecnología Agraria y Alimentaria
KeywordsBioavailabilitySewage sludgeSoil waterChemistryAmendmentSunflowerEnvironmental chemistryOrganic matterBiosolidsAgronomySewageHelianthus annuusEnvironmental scienceEnvironmental engineeringBiologySoil science

Abstract

fetched live from OpenAlex

The recycling of sewage sludge to agricultural land results in the slow accumulation of potentially toxic heavy metals in soils. A greenhouse experiment was conducted to determine the bioavailability of Cu, Ni, Pb and Zn applied to soils in urban anaerobically stabilized sewage sludge. The soils were Lithic Haplumbrept (Lh), Calcixerollic Xerochrept (Cx1 and Cx2) and Paralithic Xerorthent (Px). Sunflower plants (Helianthus annuus L) were grown in the soils following amendment with the sludge. The addition of sewage sludge markedly increased the average dry weight of the plants in the soils that had lower yields without sludge addition (Lh, Cx2, and Px). The acid pH of the Lh soil favoured the bioavailability of Zn from sewage sludge. The bioavailability of Cu was greater in the alkaline soils than in the acidic soil (Lh), which can be attributed to the high organic matter content of the Lh soil which complexes Cu and impairs its uptake by the plants. The concentration of metals in the plants increased with the sewage sludge dose. The effect of the soil type on the metal concentration in plants was greater that the effect of the dose. Key words: Soils, sewage sludge, heavy metals, bioavailability, sunflower

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.217
Teacher spread0.196 · 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.

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

Citations80
Published2002
Admission routes1
Has abstractyes

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