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Record W1972666418 · doi:10.1080/03601234.2013.836871

Photocatalytic degradation of agricultural antibiotics using a UV-LED light source

2013· article· en· W1972666418 on OpenAlexafffund
Aramice Y. S. Malkhasian, Maryam Izadifard, Gopal Achari, Cooper H. Langford

Bibliographic record

VenueJournal of Environmental Science and Health Part B · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhotocatalysisMineralization (soil science)Degradation (telecommunications)Environmental chemistrySlurryContaminationAbsorbanceChemistryPulp and paper industryNuclear chemistryEnvironmental scienceChromatographyCatalysisEnvironmental engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

With a view to developing a UV-LED photocatalytic reactor for small and remote water systems, the degradation of three representative agriculturally produced contaminants, two antibiotics and an endocrine disruptor hormone, was evaluated in a bench 365 nm LED photoreactor using a slurry of the well-known Degussa P25® (TiO2) as photocatalyst. Use of an additional electron capture additives O2 and H2O2 was also assessed. Loss of the parent organic compounds was tracked by HPLC or UV absorbance and mineralization, where feasible, was studied with TOC analysis with conventional instrumentation. In all cases, degradation is significant with moderate light dose. Lab data suggest log reduction with light delivery less than 2.2 kWhr per cubic meter light delivery.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.038
GPT teacher head0.307
Teacher spread0.268 · 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

Citations21
Published2013
Admission routes2
Has abstractyes

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Same venueJournal of Environmental Science and Health Part BSame topicPharmaceutical and Antibiotic Environmental ImpactsFrench-language works237,207