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Record W2886287647 · doi:10.11159/icepr18.163

Determination of Soluble/Exchangeable Metals in Peri-urban Farmland (Ribeira dos Covões) of Central Portugal

2018· article· en· W2886287647 on OpenAlexvenueno aff
Ryunosuke Kikuchi, Carla Ferreira, Fábio L. P. Viela

Bibliographic record

VenueProceedings of the World Congress on New Technologies · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
FundersRyukoku University
KeywordsPeriEnvironmental scienceEnvironmental chemistryChemistryArt

Abstract

fetched live from OpenAlex

Heavy metal contamination of soil is widespread, and there is a potential risk of transferring toxic metals to agricultural crops, animals and humans. The total heavy metals content can indicate the extent of contamination, but this measure is not usually an accurate indication of the phyto-toxicity; therefore, a number of recent studies have investigated the bioavailable heavy metal fractions in soils and evaluated the phyto-toxic risks for humans. Soluble and exchangeable forms of metals in the soil are the fractions available for plants. The main purpose of the present study is to quantify the total and soluble/exchangeable fractions of heavy metals (Pb, Cr, Cu and Zn) in soils of peri-urban farmland in Portugal (Ribeira dos Coves). The results show that the total heavy metals content is greater than the soluble/exchangeable content, but no clear correlations were recorded: Pb -70.8 mg/kg vs. 16.7 mg/kg, Cr -25.0 mg/kg vs. 0.0 mg/kg, Zn -383.3 mg/kg vs. 0.0 mg/kg, Cu -183.3 mg/kg vs. 0.0 mg/kg in a horticultural garden (site 10), and; Pb -270.8 mg/kg vs. 79.0 mg/kg, Cr -25.0 mg/kg vs. 0.0 mg/kg, Zn -12.5 mg/kg vs. 0.0 mg/kg, Cu -33.3 mg/kg vs. 29.2 mg/kg in a backyard (site 18). It is possible that the use of a single extraction procedure, in the laboratory, may not provide a proper assessment of heavy metal forms, and it is therefore advisable to combine different extraction methods in order to correctly perform a risk-based evaluation.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.018
GPT teacher head0.246
Teacher spread0.228 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

Explore more

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