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Record W4233490457 · doi:10.32920/ryerson.14654799

Design of wastewater treatment plant and nutrients recovery

2021· preprint· en· W4233490457 on OpenAlexaff
Ashish Gupta

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSedimentationWastewaterSewage treatmentCapital costCapital (architecture)Environmental scienceNutrientWaste managementOperations managementBusinessEnvironmental engineeringEngineeringSediment

Abstract

fetched live from OpenAlex

The objective was to design a municipal wastewater treatment plant with primary sedimentation and without primary sedimentation and then compare the savings in capital cost. The project discussed the design procures for the various units such as preliminary units, secondary units and tertiary units. The parametric cost estimation concept is utilized to arrive at the capital cost savings. The literature review includes the various methods to recover the nutrients from the wastewater. The literature review also includes various measures to be taken for performance improvement of municipal wastewater treatment plants. This project concludes that capital cost savings of about twenty percent can be achieved by not providing the primary sedimentation.

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 score0.608
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.025
GPT teacher head0.212
Teacher spread0.187 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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