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Environmental risk and possibilities of ciprofloxacin phytoremediation

2022· article· en· W4285810238 on OpenAlexaboutno aff
Тимофеева Светлана Семеновна, O V Tyukalova, S S Timofeev

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsPhytoremediationCiprofloxacinWastewaterAquatic environmentAquatic plantEnvironmental chemistryAquatic ecosystemAntibioticsBiologyBotanyEnvironmental scienceEnvironmental engineeringChemistryEcologyMicrobiologyHeavy metalsMacrophyte

Abstract

fetched live from OpenAlex

Abstract The article considers antibiotics of the quinolone series, their using and mechanism of action. Based on the literature data, their application, distribution mechanisms, accumulation and behavior in environmental objects are considered. It is noted that the role of aquatic plants in the processes of biochemical degradation has not been sufficiently studied. Under the conditions of a laboratory model experiment, we studied the patterns of ciprofloxacin elimination by hydatophytes, i.e., aquatic plants completely submerged in water (Canadian pondweed, rigid hornwort, Eurasian watermilfoil). The spectral characteristics of ciprofloxacin were studied and quantitative estimates of the absorption of the antibiotic from solutions with submerged aquatic plants were made. We calculated elimination rate constants and phytoremediation potential. It was found that the rate of elimination of ciprofloxacin and phytoremediation potential depended on the type of aquatic plant and the initial concentration of the antibiotic. The highest elimination rate was found in models containing hornwort. Based on the data obtained, a conclusion was made about the prospects for the using of hydatophytes in phytopurification systems for post-treatment of wastewater from ciprofloxacin.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.597
Threshold uncertainty score0.998

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 studies0.0010.005
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.014
GPT teacher head0.223
Teacher spread0.209 · 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.

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

Citations8
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueIOP Conference Series Earth and Environmental ScienceSame topicPharmaceutical and Antibiotic Environmental ImpactsFrench-language works237,207