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Record W2765190268 · doi:10.1680/jenes.17.00014

Hydraulic performance of a reed bed/freezing bed technology for septage dewatering

2017· article· en· W2765190268 on OpenAlexaffvenue
Chris Kinsley, Kevin J. Kennedy, Anna Crolla

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversity of GuelphUniversity of Ottawa
Fundersnot available
KeywordsCloggingEnvironmental scienceHydraulic conductivityDewateringDrainageSand filterFiltration (mathematics)Hydrology (agriculture)Environmental engineeringSoil scienceGeotechnical engineeringEcologyGeologyWastewaterSoil water

Abstract

fetched live from OpenAlex

The combined application of sludge treatment reed bed and freezing bed technology has been demonstrated to effectively dewater septage year-round under cold climate conditions in a 5-year field scale trial. Solid and hydraulic loading rates were varied from 43 to 147 kg total solids (TS)/m2/year and 1·9 to 5·9 m/year to two 187 m2 planted and one 187 m2 unplanted systems. Winter freeze–thaw conditioning was shown to consistently double filter drainage rates in spring compared with summer operating conditions at equivalent hydraulic head, indicating that freeze–thaw conditioning can restore bed hydraulic conductivity and mitigate the risk of clogging. No significant effect on system drainage was observed between planted and unplanted systems, between 7 and 21 d of dosing cycles or with solid loading rates between 49 and 144 kg TS/m2/year. However, drainage rates were shown to vary significantly with the hydraulic loading rate. A design loading rate of 2·9 m/year is recommended for septage treatment in reed bed systems operating in cold climates.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.633
Threshold uncertainty score0.348

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 studies0.0000.001
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 head0.006
GPT teacher head0.197
Teacher spread0.191 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2017
Admission routes2
Has abstractyes

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