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Acute Impact of Chlortetracycline on Nitrifying and Denitrifying Processes

2018· article· en· W2891528353 on OpenAlexafffund
Rama Pulicharla, Mehdi Zolfaghari, Satinder Kaur Brar, Patrick Drogui, Serge Auger, Mausam Verma, Rao Y. Surampalli

Bibliographic record

VenueWater Environment Research · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsPhytronix (Canada)Institut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAnoxic watersNitrificationChlortetracyclineChemistryDenitrifying bacteriaEffluentNitriteEnvironmental chemistryDenitrificationNitrateActivated sludgeSewage treatmentEnvironmental engineeringNitrogenBiochemistryAntibioticsOrganic chemistryEnvironmental science

Abstract

fetched live from OpenAlex

In the current study, sequential nitrification and anoxic experiments in synthetic municipal wastewater were exposed to 0.5 to 100 mg/L of chlortetracycline for 24 h to evaluate acute impact on the nitrification, and denitrification processes of biological treatment. Both processes were significantly (p < 0.05) inhibited at >50 mg/L of chlortetracycline, and the results revealed that nitrification was adversely affected by chlortetracycline compared with the anoxic process. In nitrification, chemical oxygen removal (COD) and ammonia oxidation kinetics were 50% inhibited at 10 mg chlortetracycline/L, and nitrite oxidation kinetics at 0.5 mg chlortetracycline/L. Likewise, in the anoxic process, 14 and 10 mg/L of chlortetracycline inhibited 50% of COD removal and nitrate reduction kinetics, respectively. In nitrification and denitrification, 90% of chlortetracycline was removed by adsorbing onto sludge suspended solids. In addition, a higher chlortetracycline concentration in anoxic effluent, compared with aerobic effluents, indicated a dissimilarity in the composition of sludge solids, pH, and biomass production for both processes.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.019
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
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.002

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 head0.091
GPT teacher head0.407
Teacher spread0.317 · 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 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

Citations14
Published2018
Admission routes2
Has abstractyes

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