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Record W2891866412 · doi:10.2495/safe-v8-n4-528-535

Field study on SS discharge from combined sewer system of highly urbanized area

2018· article· en· W2891866412 on OpenAlexvenueno aff
Tadaharu ISHIKAWA, Shin Miura, Reiko Yamamoto

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2018
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, Agriculture Analysis
Canadian institutionsnot available
FundersTokyo Metropolitan Government
KeywordsEnvironmental scienceField (mathematics)Sanitary sewerCivil engineeringEnvironmental engineeringEngineering

Abstract

fetched live from OpenAlex

the sewage coverage rate in tokyo ward has reached 100%, however, 80% of this coverage is combined sewer systems that were built before 1980.consequently, large quantities of organic pollutants are discharged from the rainwater outlets into rivers during heavy rainfalls.the organic pollutants accumulate in downstream brackish-water regions, and water quality problems such as floating scum, foul odors, and cloudy water arise as a result of chemical changes associated with bottom water becoming anaerobic.in this study, the suspended solid (ss) runoff characteristics were investigated in the nomi river drainage basin in which a combined sewer system handles 100%.the overflow water depth and ss were measured at the weir in one rainwater outlet during two storm events, and the parameters for miKe urban software that calculates sewer runoff were adjusted in the ranges recommended by the manual so that the observation results could be reproduced well.next, the model was applied to the 38 outlets in the entire catchment of the nomi river, and the estimated discharges were input into a one-dimensional unsteady river flow model.observational results of water level and turbidity measurements within the river channel were satisfactorily reproduced by the calculation.

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 score0.341
Threshold uncertainty score0.437

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 head0.005
GPT teacher head0.197
Teacher spread0.193 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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