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Record W4387260776 · doi:10.1080/01919512.2023.2264339

Microcystin-LR Removal by Ozone (O <sub>3</sub> ) and Vacuum-UV (VUV): The Effect of Chloride Ions

2023· article· en· W4387260776 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOzone Science and Engineering · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsUniversity of British Columbia
FundersQatar National Research Fund
KeywordsChlorineOzoneChlorideChemistryEnvironmental chemistryUltravioletNuclear chemistryMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Removal of microcystin-LR (MC-LR) by ozone (O3), vacuum-UV (VUV), and their combination was investigated in the presence of chloride as one of the main solutes present in water. In general, the combined VUV/O3 process provided the greatest MC-LR removal, with the presence of chloride enhancing the removal efficacy. Formation of chlorine radical species was the primary reason for the observed improvement. The order of MC-LR removal by different processes using UV fluence of around 300 mJ cm−2, ozone dose of 0.1 mg L−1, and chloride concentration of 120 mg L−1 was as follows: VUV/O3/Chloride > VUV/O3 > VUV/Chloride > VUV > O3. Comparing MC-LR removal by O3, VUV and VUV/O3 in synthetic lab samples, spiked with Suwannee River NOM and natural water samples of the same organic concentration, showed the significance of background organics in scavenging ozone in the process. For a given ozone dosage, MC-LR removal by O3 or VUV/O3 in natural water was lower than that in the synthetic water samples. The standalone VUV was not affected and the MC-LR removals were identical in both synthetic and natural waters.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score0.382

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 science0.0000.000
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 head0.003
GPT teacher head0.178
Teacher spread0.175 · 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