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Record W2039300721 · doi:10.1260/0263-6174.30.6.521

Evaluation of Biological Treatments for the Adsorption of Phenol from Polluted Waters

2012· article· en· W2039300721 on OpenAlexaff
Abdelkader Namane, Oumessaad Ali, Hubert Cabana, Amina Hellal

Bibliographic record

VenueAdsorption Science & Technology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsChemistryPhenolBacteriaAdsorptionCalcium alginateActivated carbonChromatographyBiomass (ecology)Nuclear chemistryCalciumEnvironmental chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

In this study, we describe the efficiency of three biological techniques (using Pseudomonas aeruginosa) for the removal of phenol from polluted water. We explore the possibilities of elimination with free bacteria present in solution, fixed bacteria on granular activated carbon (GAC) and immobilized bacteria in calcium alginate beads. Our study results show that for all the three methods the removal of phenol from solution (300 mg l −1 ) is complete. The kinetic constants for phenol removal are equivalent for two methods, namely, bacteria fixed on the GAC and those immobilized in calcium alginate beads (≈0.2 h −1 ), while for the free bacteria in solution, it is about half of this value. We also report how the biomass production in solution depends on the method applied. The concentration seems to act as a regulator for the amount of bacteria released in solution.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.298
Teacher spread0.242 · 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 designBench or experimental
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

Citations6
Published2012
Admission routes1
Has abstractyes

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