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Record W2171211516 · doi:10.1139/w09-104

The impact of pond depth and environmental conditions on sunlight inactivation of Escherichia coli and enterococci in wastewater in a warm climate

2009· article· en· W2171211516 on OpenAlexvenueno aff
Ynoussa Maïga, Joseph Wethé, Kokou Denyigba, Aboubakar S. Ouattara

Bibliographic record

VenueCanadian Journal of Microbiology · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicFecal contamination and water quality
Canadian institutionsnot available
Fundersnot available
KeywordsMicrocosmSunlightEscherichia coliEffluentWastewaterBiologyIndicator organismNutrientMicrobiologySettlingEnvironmental scienceAnimal scienceEnvironmental chemistryEcologyChemistryEnvironmental engineeringPhysicsBiochemistry

Abstract

fetched live from OpenAlex

Microcosm experiments were carried out under dark and real sunlight conditions in Ouagadougou (Burkina Faso) to investigate the survival of faecal indicators (Escherichia coli and enterococci) in secondary wastewater. Light damage was estimated by loss of bacterial culturability. The results clearly show that sunlight has a deleterious effect on the survival of both indicators. The mean dark inactivation coefficients for E. coli and enterococci were 0.045 and 0.047 h(-1), respectively, whereas inactivation coefficients in the shallowest microcosm (0.1 m) in illuminated conditions were 0.796 and 0.559 h(-1), respectively. No significant effect of pond depth (0.1-0.9 m) on the inactivation of both indicators was observed in the dark. However, the effect of depth was significant in the microcosms exposed to sunlight, probably because of attenuation. In illuminated conditions, enterococci were broadly inactivated more rapidly than E. coli (T90 = 26.81 h for E. coli and 15.67 h for enterococci in the 0.4 m microcosm). However, E. coli presented greater variability in the survival capabilities, suggesting difficulties in interpreting data using only E. coli as an indicator. Therefore, the use of both indicators together should be advisable for the assessment of effluent quality from waste stabilization ponds in the Sahelian region.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.008
GPT teacher head0.231
Teacher spread0.223 · 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.

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

Citations36
Published2009
Admission routes1
Has abstractyes

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