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Freeze-Thaw Treatment of Membrane Concentrates Derived from Kraft Pulp Mill Operations

2001· article· en· W2079874714 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

VenueJournal of Cold Regions Engineering · 2001
Typearticle
Languageen
FieldEngineering
TopicFreezing and Crystallization Processes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsKraft paperKraft processPulp (tooth)cardboardPulp and paper industryEffluentPaper millMaterials scienceWaste managementEnvironmental scienceComposite materialEnvironmental engineering

Abstract

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Freeze thaw was studied as a waste treatment method for concentration and volume reduction of contaminated waste concentrates that are derived from the use of membrane technology in the treatment of high strength Kraft pulp mill effluents. Unidirectional freezing experiments were conducted to simulate seminatural freezing, in which the independent variables—freezing rate, time frozen, storage temperature, concentration, liquid depth, thawing rate and method of thawing—were examined for their relative importance. Method of thawing followed by freezing rate, rate of thawing, storage temperature, and time frozen were identified as the most important independent variables that contribute significantly to treatment performance. Under ideal conditions, freeze thaw was shown to effectively concentrate and separate the constituent matter of alkaline, extraction-stage membrane concentrate to achieve color removals as high as 73% in the top 70% liquid fraction. The results suggest a new field of use for freeze thaw as a waste treatment process for the management of high strength liquid wastes amenable to mechanical coagulation by freezing.

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)0.000
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 score0.573
Threshold uncertainty score0.603

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 communication0.0000.000
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 head0.012
GPT teacher head0.202
Teacher spread0.190 · 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