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Record W2279245507 · doi:10.2166/9781780403090

Assessment of Technologies for Screening, Floatable Control and Screenings Handling

2015· article· en· W2279245507 on OpenAlexaff
James B. Stephenson, B. Gall, C. Mroczek, M. Newbigging, J. A. Parker

Bibliographic record

VenueWater Intelligence Online · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsHydromantis Environmental Software Solutions (Canada)
Fundersnot available
KeywordsWaste managementCombined sewerEnvironmental scienceWastewaterSewage treatmentEngineeringProcess engineeringStormwater

Abstract

fetched live from OpenAlex

Screening and floatable controls are used at wastewater treatment, sanitary sewer overflow (SSO) and combined sewer overflow (CSO) locations. Screening is a preliminary treatment step and used to protect downstream equipment. Screening and floatable control is a means of removing visible inorganic and non-biodegradable organic material from further treatment processes or discharge. In CSO applications screening and floatable control avoids the discharge of visible objectionable material.Screening and screenings handling are among the unpopular processes to deal with due to aesthetic and health concerns, odor, and the historically questionable reliability of the equipment. The operation and maintenance can be costly and labor intensive. Existing and proposed environmental regulations require floatables control from CSO and sanitary sewer overflow (SSO) sites, often at locations that are unmanned.Screening was one of the first methods of removing large solids from wastewater. Coarse screens are used to protect equipment and remove larger objects from the wastewater. Fine screens will remove smaller material and some amount of organic material. Very fine screens or microscreens remove even smaller material and potentially replacing grit removal and primary treatment.The type of screening device used at a particular location depends on the screen opening required and the flow rate. Peripheral devices, such as screenings washing and compaction, are often required to meet final disposal requirements. Generally, the finer the screen, the smaller the material to be removed, and the larger the unit to reduce the head loss through the process.WERF identified the need to assess traditional and emerging screening technologies and provide a review of practices to ensure that new and upgraded facilities will be up-to-date, operator-friendly and reliable. This work reviews applications and issues associated with screening and their peripheral equipment and their use at various locations with a variety of treatment and handling goals.This title belongs to WERF Research Report SeriesISBN: 9781780403090 (eBook)ISBN: 9781843396406 (Print)

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 score0.691
Threshold uncertainty score0.502

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.068
GPT teacher head0.329
Teacher spread0.261 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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