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Record W1980789886 · doi:10.2166/ws.2011.076

Biological aspects of slow sand filtration: past, present and future

2011· article· en· W1980789886 on OpenAlexaff
Sarah–Jane Haig, Gavin Collins, Robert L. Davies, Caetano C. Dorea, Christopher Quince

Bibliographic record

VenueWater Science & Technology Water Supply · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsContext (archaeology)Environmental scienceFiltration (mathematics)Biochemical engineeringSlow sand filterWater qualityEcologyWater treatmentEnvironmental engineeringBiologyEngineeringMathematics

Abstract

fetched live from OpenAlex

For over 200 years, slow sand filtration (SSF) has been an effective means of treating water for the control of microbiological contaminants in both small and large community water supplies. However, such systems lost popularity to rapid sand filters mainly due to smaller land requirements and less sensitivity to water quality variations. SSF is still a particularly attractive process because its operation does not require chemicals or electricity. It can achieve a high level of treatment, which is mainly attributed to naturally-occurring, biochemical processes in the filter. Several microbiologically-mediated purification mechanisms (e.g. predation, scavenging, adsorption and bio-oxidation) have been hypothesised or assumed to occur in the biofilm that forms in the filter but these have not yet been comprehensively verified. Thus, SSFs are operated as ‘black boxes’ and knowledge gaps pertaining to the underlying ecology and ecophysiology limit the design and optimisation of the technology. The objective of this review is to outline the biological aspects of SSF in to the context of recent developments in molecular microbial ecology.

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 categoriesInsufficient payload (model declined to judge)
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 score0.456
Threshold uncertainty score1.000

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.197
Teacher spread0.186 · 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.

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

Citations90
Published2011
Admission routes1
Has abstractyes

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