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Record W3096206071 · doi:10.1080/26395940.2020.1842251

Assessment of heavy metals pollution in sediment at the Omaruru River basin in Erongo region, Namibia

2020· article· en· W3096206071 on OpenAlexaboutno aff
Sylvanus Ameh Onjefu, Fatima Shaningwa, Julien M. Lusilao, J. Abah, Euodia Hess, Habauka M. Kwaambwa

Bibliographic record

VenueEnvironmental Pollutants and Bioavailability · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
Fundersnot available
KeywordsSedimentEnvironmental chemistryHeavy metalsPollutionEnvironmental scienceInductively coupled plasmaMetalContaminationChemistryGeologyGeomorphologyEcologyPlasmaPhysics

Abstract

fetched live from OpenAlex

This study determined the contamination levels of some heavy metals: Mn, Cr, Cu, Ni, Fe, As, and Al obtained from the delta basin of Omaruru River, using Inductively Coupled Plasma-Optical Emission Spectrophotometer (Optima 8000, Perkin Elmer). Chemical analysis showed that the sediments samples have metal concentrations ranging from 88 to 128mg/kg for Mn, 3973 to 4369mg/kg for Fe, 96 to 107mg/kg for Cr, 6 to 9mg/kg for Cu, 9 to 11mg/kg for Ni, 17 to 19mg/kg for As, and 11750 to 9002mg/kg for Al. The concentrations of Cr and As showed suggest that the sediment samples were heavily polluted according to United States Environmental Protection Agency's regulatory guidelines of the heavy metals. Additionally, the concentrations of Cr and As in the sediments exceeded the effect low range, effect medium range, threshold effect level, and the probable effect level proposed by the Canadian Council of Ministers of the Environment.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.016
GPT teacher head0.236
Teacher spread0.220 · 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 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

Citations27
Published2020
Admission routes1
Has abstractyes

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