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Record W2892798139 · doi:10.1680/jenes.18.00024

Ultrasonic freezing for solubilisation of sludge organic matter and enhanced conditioning

2018· article· en· W2892798139 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2018
Typearticle
Languageen
FieldEngineering
TopicFreezing and Crystallization Processes
Canadian institutionsLakehead University
Fundersnot available
KeywordsOrganic matterConditioningSewage sludgeCongelationChemistryChemical oxygen demandFreezing behaviorPulp and paper industrySewageSewage treatmentEnvironmental scienceEnvironmental engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Two new freezing treatment methods (partial ultrasonic freezing and combined ultrasonic freezing) were examined for their effectiveness on both solubilisation of organic matter and enhancement of sewage sludge conditioning. The treatment efficiency of the new freezing methods was compared with that of conventional freezing. The test results revealed that the capacity of the two new freezing methods on solubilisation of sludge organic matter was comparable to that of conventional freezing with three to five freezing and thawing cycles. About five to seven times increase in sample soluble chemical oxygen demand and soluble protein concentrations were observed after treatment using the three different freezing methods. Significant improvement in sludge conditioning was also achieved; more than 80% reduction in settled sludge volume was noted in the freezing-treated samples. Overall, all freezing techniques examined showed great potential as effective treatment methods that could not only enhance sludge conditioning but also solubilise organic matter.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.234

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 head0.004
GPT teacher head0.180
Teacher spread0.176 · 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