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Record W2462571538 · doi:10.2175/106143008x266814

Petroleum Refinery Secondary Effluent Polishing Using Freezing Processes—Toxicity and Organic Contaminant Removal

2008· article· en· W2462571538 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

VenueWater Environment Research · 2008
Typearticle
Languageen
FieldEngineering
TopicFreezing and Crystallization Processes
Canadian institutionsUniversity of AlbertaLakehead University
Fundersnot available
KeywordsEffluentChemistryChemical oxygen demandEnvironmental chemistryRefineryToxicityContaminationPulp and paper industryEnvironmental scienceEnvironmental engineeringWastewaterEcology

Abstract

fetched live from OpenAlex

A petroleum refinery secondary effluent was treated using two freezing techniques--spray freezing and unidirectional downward freezing (UDF). The freezing processes were effective to remove toxicity and total organic carbon (TOC)- and chemical oxygen demand (COD)-causing materials in the effluent. Agitation of the liquid during UDF significantly improved the impurity separation efficiency; 85 to 96% removal of TOC and COD was achieved without any pretreatment and freezing only 70% of the feed water. The treatment efficiency of the spray freezing was at the same level as that of UDF without mixing. The spray ice with longer storage time released more contaminants with early meltwater. The initial contaminant concentration of the feed water and the freezing temperatures (-10 degrees C and -25 degrees C) had no significant influence on the treatment efficiency. A small fluctuation in effluent TOC concentration caused a dramatic change in effluent toxicity (Microtox). The effective concentration (EC20) (Microtox) was effective in detecting effluent toxicity.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.714

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.241
Teacher spread0.206 · 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