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Record W6903169751 · doi:10.1021/es5017558.s001

Application\nof a Solar UV/Chlorine Advanced Oxidation\nProcess to Oil Sands Process-Affected Water Remediation

2016· article· en· W6903169751 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsnot available
Fundersnot available
KeywordsOil sandsTailingsDegradation (telecommunications)BiodegradationEnvironmental remediationMicrobial biodegradationOrganic chemicalsRaw water

Abstract

fetched live from OpenAlex

The\nsolar UV/chlorine process has emerged as a novel advanced oxidation\nprocess for industrial and municipal wastewaters. Currently, its practical\napplication to oil sands process-affected water (OSPW) remediation\nhas been studied to treat fresh OSPW retained in large tailings ponds,\nwhich can cause significant adverse environmental impacts on ground\nand surface waters in Northern Alberta, Canada. Degradation of naphthenic\nacids (NAs) and fluorophore organic compounds in OSPW was investigated.\nIn a laboratory-scale UV/chlorine treatment, the NAs degradation was\nclearly structure-dependent and hydroxyl radical-based. In terms of\nthe NAs degradation rate, the raw OSPW (pH ∼ 8.3) rates were\nhigher than those at an alkaline condition (pH = 10). Under actual\nsunlight, direct solar photolysis partially degraded fluorophore organic\ncompounds, as indicated by the qualitative synchronous fluorescence\nspectra (SFS) of the OSPW, but did not impact NAs degradation. The\nsolar/chlorine process effectively removed NAs (75–84% removal)\nand fluorophore organic compounds in OSPW in the presence of 200 or\n300 mg L<sup>–1</sup> OCl<sup>–</sup>. The acute toxicity\nof OSPW toward Vibrio fischeri was\nreduced after the solar/chlorine treatment. However, the OSPW toxicity\ntoward goldfish primary kidney macrophages after solar/chlorine treatment\nshowed no obvious toxicity reduction versus that of untreated OSPW,\nwhich warrants further study for process optimization.

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 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 score0.351
Threshold uncertainty score0.976

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0800.025

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 head0.009
GPT teacher head0.243
Teacher spread0.233 · 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
Published2016
Admission routes1
Has abstractyes

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