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Record W1492712956

Évaluation du niveau de pollution par les métaux lourds des lacs Bini et Dang, Région de l'Adamaoua, Cameroun

2014· article· fr· W1492712956 on OpenAlexaboutno aff
Oumar Barkai, Nga Léopold Ekengele, Ondoa Augustin Désiré Balla

Bibliographic record

VenueAfrique Science Revue Internationale des Sciences et Technologie · 2014
Typearticle
Languagefr
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsForestryGeography
DOInot available

Abstract

fetched live from OpenAlex

La presente etude s’est donnee a pour objectif principal d’evaluer le niveau de pollution metallique des lacs Bini et Dang (Ngaoundere, Cameroun) a travers l’analyse des eaux et des sediments de surface. La concentration des metaux lourds (Ni, Cr, Fe, Pb, Cd, Zn) a ete mesuree par spectrophotometrie d’absorption atomique. Des resultats obtenus, il ressort que les elements Ni, Fe, Cr, Pb et Cd ont des teneurs elevees dans les eaux des deux lacs compares aux normes OMS sur les eaux de boisson et aux normes canadiennes sur la protection de la vie aquatique. Afin d’evaluer le niveau de contamination des sediments des deux lacs, le facteur d’enrichissement (FE) et l’indice de geoaccumulation (I-geo) ont ete calcules. Ainsi, le cadmium presente des FE forts a tres forts alors que le plomb affiche des FE moderes a forts. L’I-geo varie d’une forte contamination a une contamination extreme pour le cadmium. Cet index est modere a fort pour le plomb et modere pour le fer. Les sediments du lac de Dang sont dans l’ensemble les plus contamines en metaux lourds par rapport a ceux du lac Bini. Les activites agricoles, les rejets des eaux usees domestiques et les decharges incontrolees sont consideres comme principales sources de pollution des deux lacs.

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)0.014
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.020
Scholarly communication0.0010.003
Open science0.0020.001
Research integrity0.0000.001
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 head0.114
GPT teacher head0.346
Teacher spread0.232 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations13
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueAfrique Science Revue Internationale des Sciences et TechnologieSame topicWater Quality and Pollution AssessmentFrench-language works237,207