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Record W2986693746 · doi:10.1139/cjce-2019-0269

Effect of different sediment dewatering techniques on subsequent particle sizes in industrial derived effluent

2019· article· en· W2986693746 on OpenAlexaffvenueabout
Mahmood Alimohammadi, Hayden A. Tackley, Craig B. Lake, Ian Spooner, Tony R. ‎Walker, Rob Jamieson, Chee‐Yuen Gan, Kevin Bossy

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsBishop Water Technologies (Canada)Acadia UniversityDalhousie University
Fundersnot available
KeywordsDewateringGeotextileSedimentationSedimentParticulatesFiltration (mathematics)Environmental scienceEffluentContext (archaeology)Environmental engineeringWaste managementGeotechnical engineeringGeologyChemistryEngineering

Abstract

fetched live from OpenAlex

A paucity of literature has compared geotextile dewatering methods to more conventional dewatering methods (i.e., centrifuge, sedimentation) in the context of how geotextile dewatering performs at reducing particulate matter in dewatering effluent. Particulate matter is the primary source of inorganic and organic contaminants (i.e., dioxins and furans) in an unconsolidated sediment (estimated 577 000 m 3 ) that has accumulated in a wastewater stabilization basin in Nova Scotia, Canada. Physical and chemical properties of contaminated sediment were initially characterized, and subsequent laboratory experiments were carried out for three common dewatering methods: sedimentation, centrifugation, and geotextile filtration. Filtrate quality of suspended solids (number, particle size distribution of particles) was examined for differences based on three dewatering techniques assessed. All three methods provided effective removal of particulate matter during dewatering, but geotextile dewatering could be a more cost-effective and practical solution for dewatering of these sediments.

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)0.000
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 score0.497
Threshold uncertainty score0.542

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 head0.007
GPT teacher head0.191
Teacher spread0.184 · 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

Citations19
Published2019
Admission routes3
Has abstractyes

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