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Record W2766448552 · doi:10.2166/ws.2017.214

Effect of vacuum UV irradiation on the concentration of dissolved cyanobacterial toxin microcystin-LR

2017· article· en· W2766448552 on OpenAlexaff
Pranav Chintalapati, Madjid Mohseni

Bibliographic record

VenueWater Science & Technology Water Supply · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAlkalinityDegradation (telecommunications)ChemistryChlorideDissolved organic carbonIrradiationEnvironmental chemistryPhotodissociationRadicalNuclear chemistryPhotochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract This study investigated the capability of vacuum UV to reduce the concentration of cyanobacterial toxin microcystin-LR (MC-LR) using low-pressure Hg lamps emitting 185 nm and 254 nm light. A collimated beam setup was used to irradiate samples of MC-LR solutions prepared in Milli-Q® water. The impact of competing water compounds was tested using solutions containing dissolved organic carbon (DOC), alkalinity (NaHCO3), and chloride (NaCl). Results showed that MC-LR in pure water at typical concentrations found in cyanobacterial bloom waters (17 and 40 μg/L) could be reduced below detection limits (0.5 μg/L) within one minute of irradiation time by a UV dose less than 40 mJ/cm2. A solution with a much higher initial concentration of MC-LR (870 μg/L) did show a reduced degradation rate. The presence of competing compounds does appear to reduce observed MC-LR degradation rates with the greatest impact caused by DOC followed by alkalinity followed by chloride. MC-LR degradation appears to occur by both direct photolysis by 254 nm photons and by advanced oxidation by hydroxyl radicals generated from 185 nm photons. Vacuum UV has shown promising capability at reducing MC-LR concentration.

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 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 score0.096
Threshold uncertainty score0.655

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 studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.004
GPT teacher head0.215
Teacher spread0.211 · 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.

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

Citations8
Published2017
Admission routes1
Has abstractyes

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