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Record W3214000744

Sorption of trace Ciprofloxacin to environmental media

2021· article· en· W3214000744 on OpenAlexaff
Ryan Gallagher

Bibliographic record

VenueStudent Research Proceedings · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsMacEwan University
Fundersnot available
KeywordsSorptionEnvironmental chemistryChemistryEnvironmental remediationSoil waterEnvironmental scienceEffluentBiocharContaminationEnvironmental engineeringOrganic chemistryAdsorptionSoil science
DOInot available

Abstract

fetched live from OpenAlex

The increased usage of pharmaceuticals, especially antibiotics has unintentionally resulted in their increased introduction into the environment, especially via effluent into waterways from wastewater treatment plants. Many of these drugs can have significant detrimental effects on local wildlife and flora and have the potential to be introduced into the food chain. Sorption of these trace pharmaceuticals to environmental surfaces is of interest as it plays a key role in their mobility and remediation. In this study the detection, characterisation and quantification of the sorption of Ciprofloxacin (cipro) to goethite, a iron oxide mineral commonly found in soils, and biochar. The sorption was tested from pH 3-10 and was analyzed using HPLC-MS and FTIR spectroscopy. Sorption of the cipro was confirmed and 3 potential breakdown products were discovered. These breakdown products are likely produced by oxidation at the mineral surface. Sorption efficiency was determined using HPLC-MS, and found to be most efficient from PH 6-8. The results of this preliminary study will be used in future works in predicting the mobility of trace pharmaceuticals and their breakdown products in environment. Department: Physical Sciences Faculty Mentor: Dr. Janice Kenney

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 score0.295
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 studies0.0000.000
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 head0.116
GPT teacher head0.419
Teacher spread0.303 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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