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Record W1977236680 · doi:10.1081/css-120017419

Phyto-extraction of Copper, Iron, Manganese, and Zinc from Environmentally Contaminated Sites in Ethiopia, with Three Grass Species

2003· article· en· W1977236680 on OpenAlexafffundabout
Fisseha Itanna, Bruce Coulman

Bibliographic record

VenueCommunications in Soil Science and Plant Analysis · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Micronutrient Interactions and Effects
Canadian institutionsAgriculture and Agri-Food Canada
FundersInternational Development Research CentreUniversity of Saskatchewan
KeywordsManganeseZincCopperEnvironmental chemistryContaminationExtraction (chemistry)ChemistryEnvironmental scienceBiologyEcology

Abstract

fetched live from OpenAlex

Abstract Rhodegrass (Chloris gayana cv. Kallide) and forage setaria (Setaria sphacelata cv. Kazungula), two indigenous grass species, and oat (Avena sativa L.), a recently introduced forage crop, were grown on contaminated sites in Ethiopia, to determine their potential in removing copper (Cu), iron (Fe), manganese (Mn), and zinc (Zn) from the soils. Soil pH influenced the availability and plant uptake of micronutrients; therefore, the grasses grown at the industrial waste site with a high pH, had the lowest micronutrient concentrations. Setaria and rhodesgrass had higher concentrations of all the micronutrient metals than oat. Rhodesgrass and setaria accumulated concentrations of Fe and Mn, normally considered toxic to many crops, without developing chlorotic symptoms and yield suppressions. Iron in setaria and Zn in rhodesgrass declined significantly with advancing maturity. Considering biomass yield and tissue concentration, it was found that setaria removed the greatest quantity of micronutrients per unit area of soil. Keywords: PhytoextractionIndustrial wasteMunicipal wastesIndigenous speciesEthiopia Acknowledgments We acknowledge the ESTC (Ethiopian Science and Technology Commission) and ENDA (Environment Development Action) Ethiopia, for covering the field expenses in Ethiopia. The interest, cooperation and support given by Mr. Camille de Stoop, the country coordinator of ENDA, made the field work a success. Special thanks goes to W/o Amakelech Bogale, from ENDA Ethiopia, for providing unreserved technical assistance during the field work. We are very thankful to Mrs. Gisele Morin-Labatut, who facilitated support from IDRC to cover travel cost and part of the laboratory expenses. The ILRI is acknowledged for supplying the planting materials and for the weather data. We are also very grateful for access to facilities, and excellent cooperation by the Forage Crops Section of the Saskatoon Research Centre of Agriculture and Agri-Food Canada. The Department of Soil Science of the University of Saskatchewan is acknowledged for its support in some of the laboratory analyses and Mr. Barry Goetz for the analytical work.

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 score0.249
Threshold uncertainty score0.882

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 studies0.0000.000
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 head0.024
GPT teacher head0.236
Teacher spread0.213 · 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

Citations20
Published2003
Admission routes3
Has abstractyes

Explore more

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