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Record W101195619

Metal Concentrations in Sediments of the Ogun River, Nigeria

2013· article· en· W101195619 on OpenAlexaboutno aff
Anslem Diayi, Gbadebo A.M., Adekunle I.M., Awomeso J.A.

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsSedimentEnvironmental chemistryTrace metalEnvironmental scienceOgun stateInductively coupled plasmaHydrology (agriculture)MetalChemistryGeologyGeography
DOInot available

Abstract

fetched live from OpenAlex

Sediment samples were collected from various locations along the Ogun River basin after which they were subjected to physicochemical analysis in order to determine the concentration of metals in the sediments as well as other sediment characteristics like pH, texture, cation exchange capacity etc. The samples were collected using a Van Veen Sediment grab after which they were stored in labeled polythene bags and transported in ice packed coolers to the laboratory. They were air dried, sieved and hand pulverized. Sediment trace metal analysis was determined using the Inductively Coupled Plasma Optical Emission Spectrometry. Data obtained were subjected to Analysis of Variance (ANOVA). From the results obtained, Cd (1.00 ppm) from Lafenwa sediment exceeded the Environment Canada Sediment Quality Guideline (ECSQG) limit of 0.60 ppm. Pb values from Akin Olugbade (46.00 ppm), Lafenwa (323.00 ppm), Copper (46.00 ppm) from Lafenwa, sediment Hg values obtained ranging from 0.945-0.975 ppm were all reported to be higher than the ECSQG limits of 35ppm for Pb, 35.7 ppm for Cu and 0.17 ppm for Hg respectively. There is need to monitor Cd values in sediment, from all the locations particularly Lafenwa so that appropriate measures can be put in place to reduce increase in Cd concentrations in the Ogun River basin especially from anthropogenic sources such as indiscriminate dumping of wastes into the river. Other metals like Pb, Cu and Hg should also be monitored frequently.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.097
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0500.000

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 head0.196
GPT teacher head0.513
Teacher spread0.316 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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