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Record W2178846604 · doi:10.1139/l2012-099

Modelling and application of an uncovered freezing bed technology for septage treatment

2012· article· en· W2178846604 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicFreezing and Crystallization Processes
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSnowEnvironmental scienceSnow removalSnow coverEnvironmental engineeringHydrology (agriculture)Sewage treatmentSeptic tankMeteorologyGeotechnical engineeringGeologyGeography

Abstract

fetched live from OpenAlex

A series of pilot scale freezing bed experiments were conducted to evaluate and model the freeze–thaw treatment of septic tank sludge (septage). Filtrate quality is similar to a low strength domestic wastewater and the sludge cake has a dry matter content of 25% with E. coli numbers below 2.0 × 10 6 CFU/g dry solids. Experimental results showed no impact of snow cover on bed performance in a region with moderate snowfall (1.3–1.6 m) as new layers of sludge effectively melted any accumulated snow; suggesting that it is not necessary to cover the bed or remove the snow in areas where sludge dosing exceeds snowfall. Both freezing and thawing processes were successfully modelled with readily available climatic data. Model output for North American climatic conditions indicates that the freezing bed technology can be widely applied throughout the northern United States and Alaska and most of Canada with the exception of coastal areas and southern Ontario.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.759
Threshold uncertainty score0.401

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 head0.008
GPT teacher head0.188
Teacher spread0.179 · 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