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Record W2038384831 · doi:10.5539/esr.v1n2p152

Laboratory Study of Conductive Properties of Contaminated Riverbed Sands in Ado-Odo Ota Local Government Area of Ogun State, Nigeria

2012· article· en· W2038384831 on OpenAlexvenueno aff
Olukayode D. Akinyemi, Jamiu A. Rabiu, Vitalis Chidi Ozebo, O. A. Idowu

Bibliographic record

VenueEarth Science Research · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Resources Studies
Canadian institutionsnot available
Fundersnot available
KeywordsContaminationOgun stateCaustic (mathematics)Hydraulic conductivityEnvironmental scienceThermal conductivityEnvironmental engineeringMetreCurrent (fluid)Hydrology (agriculture)Materials scienceGeotechnical engineeringSoil scienceGeologyLocal governmentGeographyComposite materialArchaeologySoil water

Abstract

fetched live from OpenAlex

As industrial activities increase, the risk of contamination of rivers in Ado-Odo Ota Local Government Area of Ogun State in Nigeria is becoming higher. In this study, riverbed sands were collected from five major rivers in Ado-Odo Ota Local Government Area, and conductivity properties were determined after the samples have been treated with varying concentration of petrol, engine oil, diesel, caustic soda and H2SO4. HANNAN Electrical Conductivity Meter, KD2 Thermal Conductivity Meter and Constant Head Method were used to determine the electrical, thermal and hydraulic conductivities respectively. After treatment of the samples with different concentration of the contaminants, it was found that thermal, electrical and hydraulic conductivities of the samples were largely proportional to contaminants concentration in all the samples in general. However, with increase in concentration of caustic soda and H2SO4, increase in electrical and thermal conductivities of samples were observed, while increase in the concentration of other contaminants decreased the hydraulic conductivity of the samples.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.006
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.130
GPT teacher head0.320
Teacher spread0.190 · 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
Published2012
Admission routes1
Has abstractyes

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