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Record W4389136275 · doi:10.2166/wst.2023.386

Inflow and infiltration assessment of a prototype sanitary sewer network in a coastal city in China

2023· article· en· W4389136275 on OpenAlexaff
L. Ye, Qian Yu, David Z. Zhu, Biao Huang

Bibliographic record

VenueWater Science & Technology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsUniversity of Alberta
FundersKey Research and Development Program of Zhejiang Province
KeywordsInflowInfiltration (HVAC)Environmental scienceTyphoonHydrology (agriculture)GroundwaterChinaCombined sewerStormwaterSurface runoffGeographyGeologyGeotechnical engineeringMeteorologyEcology

Abstract

fetched live from OpenAlex

Abstract A 16-month monitoring program was conducted on a prototype sanitary system in a coastal city in China. The groundwater infiltration (GWI) on dry weather days and the rain-derived inflow and infiltration (RDII) on wet weather days were quantified and analyzed. The proportion of monthly averaged GWI to total flow can be as high as 70% during the observation period mainly due to the high groundwater level. The results also show that the ratio of RDII volume to total rainfall volume (defined as R-value) reaches a limited value of approximately 10% for the studied system when the total rainfall depth increases. A reference indicator Rlim for the limited R-value was proposed for assessing the conditions of sewer systems in terms of RDII. The Rlim value depends on local sewer conditions and in general, a lower Rlim value represents a better performance on RDII and vice versa. This study enriches the case studies on the performance of a specific sanitary sewer system on inflow and infiltration in a typical coastal city with exceptionally high groundwater levels, excess rainfall events in the monitoring season and possible typhoon events, which addresses the unique locational and hydrological properties of a representative coastal city.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
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 head0.009
GPT teacher head0.244
Teacher spread0.235 · 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 designObservational
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

Citations7
Published2023
Admission routes1
Has abstractyes

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