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Record W2335955401 · doi:10.2166/ws.2002.0154

Coagulation of turbid waters using Moringa oleifera seeds from two distinct sources

2002· article· en· W2335955401 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWater Science & Technology Water Supply · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsMcGill UniversityUniversité de Sherbrooke
Fundersnot available
KeywordsMoringaTurbidityAlumFlocculationCoagulationSedimentationPulp and paper industryEnvironmental scienceWater treatmentBiologyEnvironmental engineeringChemistryFood scienceEcologyEngineering

Abstract

fetched live from OpenAlex

Samples of turbid water prepared under laboratory controlled conditions were tested using natural coagulant-flocculant Moringa oleifera seeds from Burundi, Central Africa, and from Mahajanga, Madagascar. Coagulation-flocculation and sedimentation experiments were conducted using jar test equipment. For these tests, 5% Moringa oleifera solutions (w/w in water) were prepared using shelled and non-shelled seeds from the aforementioned countries. The results show that, in both cases, the shelled seeds provide much higher turbidity removal than the non-shelled ones. In addition, the volume of sludge produced was approximately 30% of that of conventional coagulants such as alum. Finally, it was concluded that seeds from Burundi were of superior quality than those of Madagascar. In fact, higher dosages of these seeds, of up to four times, were required in order to attain the same level of turbidity as the Burundi seeds.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
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 studies0.0010.003
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.025
GPT teacher head0.253
Teacher spread0.228 · 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