Bibliographic record
Abstract
Principles of Mathematical Modeling for PdNA ProcessesAbstractIn wastewater practice, there is an immense lack of knowledge in understanding the precise kinetic role of distinct bacterial populations in the overall kinetic rate of biological processes including denitrification. There is a growing need for more in-depth research to identify the functionalities of denitrifiers, especially under different operational conditions. Additionally, little effort has been devoted to developing a model that simulates all mechanisms of partial denitrification. This gap needs to be filled so that further research and engineering design could be conducted to achieve a systematic perception and optimization of partial denitrification anammox (PDNA) systems. Therefore, this study demonstrates the need for developing a mathematical model that could project all the major potential mechanisms of partial denitrification. This study discusses the development of a comprehensive and novel model that describes nitrite accumulation considering microbial ecological shifts, alternative carbon sources, feast-famine and carbon internalization, COD/N effects in kinetics, etc.Due to the lack of robust and certain outlook on partial denitrification success factors, more thorough research is essential to characterize the functionalities of denitrifiers and anammox bacteria, particularly under diverse operational conditions. This study aims to assess the development of a comprehensive and innovative model that depicts nitrite accumulation reflecting on several factors which have effects on kinetics of denitrification.SpeakerIzadi, ParinPresentation time10:50:0011:05:00Session time10:30:0012:00:00SessionPushing the Boundaries of Our Biological ModelsSession locationRoom S404a - Level 4TopicFacility Operations and Maintenance, Intermediate Level, Municipal Wastewater Treatment Design, Research and InnovationTopicFacility Operations and Maintenance, Intermediate Level, Municipal Wastewater Treatment Design, Research and InnovationAuthor(s)Izadi, ParinAuthor(s)P. Izadi <sup>1</sup>; M. Andalib <sup>2 </sup>; P. Izadi <sup>1</sup>; A. Umble <sup>3</sup>; P. Izadi <sup>4</sup>;Author affiliation(s)Stantec Consulting Ltd., Toronto, ON <sup>1</sup>; Stantec Consulting Ltd <sup>2 </sup>; Stantec Consulting Ltd. <sup>1</sup>; Stantec Consulting Ltd. <sup>3</sup>; Stantec Consulting Ltd. <sup>4</sup>;SourceProceedings of the Water Environment FederationDocument typeConference PaperPublisherWater Environment FederationPrint publication date Oct 2023DOI10.2175/193864718825159191Volume / Issue Content sourceWEFTECCopyright2023Word count8
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".