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Record W2098720902

TRACE METALS IN WATER FROM OLOGE LAGOON, LAGOS, NIGERIA

2004· article· en· W2098720902 on OpenAlexaboutno aff
K. A. Yusuf, Oladele Osibanjo

Bibliographic record

VenueASSET:An International Journal of Agricultural Sciences, Science, Environment and Technology (Series B) · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceSewageShoreHeavy metalsAquatic ecosystemCurrent (fluid)Water pollutionEnvironmental chemistryHydrology (agriculture)EstuaryEnvironmental engineeringEnvironmental protectionOceanographyGeologyChemistry
DOInot available

Abstract

fetched live from OpenAlex

The concentrations of Fe, Mn, Cu, Zn, Cd, Pb, and Ni were determined in water of Ologe lagoon on a bimonthly intervals between January 1997 and December 1998. The recorded heavy metal concentrations (except iron) were either significantly lower or within the safety limits of published averages for freshwaters; recommended standards by the EC, Canada, and USSR for the use of fisheries and aquatic life. A higher concentration of Fe reflects the natural sources due to the geology of the catchment soil. Variations in the concentrations of heavy metals in water may be due to local differences in current velocity and distance from the shore line (from sewage source of the residential sector).  The level of industrialisation in the study area is very low; hence, contribution of heavy metals from industrial sources on the heavy metal status of the lagoon is minimal.

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 score0.024
Threshold uncertainty score0.047

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 head0.011
GPT teacher head0.247
Teacher spread0.236 · 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

Citations1
Published2004
Admission routes1
Has abstractyes

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Same venueASSET:An International Journal of Agricultural Sciences, Science, Environment and Technology (Series B)Same topicWater Quality and Pollution AssessmentFrench-language works237,207