MétaCan
Menu
Back to cohort
Record W2589810788 · doi:10.1134/s0003683817010100

Modeling of anammox process with the biowin software suite

2017· article· en· W2589810788 on OpenAlexaboutno aff
А. Г. Дорофеев, Yu. A. Nikolaev, М. Н. Козлов, М. В. Кевбрина, A. M. Agarev, A. Yu. Kallistova, Н. В. Пименов

Bibliographic record

VenueApplied Biochemistry and Microbiology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsnot available
Fundersnot available
KeywordsAnammoxBioreactorAmmoniumNitrogenWastewaterActivated sludgeChemistryProcess (computing)Pulp and paper industryProcess engineeringEnvironmental scienceDenitrificationEnvironmental engineeringComputer scienceEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Mathematical modeling of the biotechnology for the removal of ammonium nitrogen from wastewater based on the anammox process was performed with the specialized BioWin software suite (EnviroSim Associates Ltd., Canada). Nitrogen removal by means of the transformation of ammonium nitrogen to molecular nitrogen was conducted in a continuous stirred bioreactor carrying both suspended and immobilized activated sludge. Both basic values of the kinetic and stoichiometric coefficients are incorporated in the BioWin software, and those that changed based on the results of the experimental studies were used for the calculations. The optimal temperature and dissolved oxygen concentration revealed by mathematical modeling were 35°C and 0.14 mg/L. The results obtained from calculations were similar to those obtained in the experiments. The calculated and experimental concentrations of ammonium, nitrites, and nitrates in the treated water were similar and comprised 10.7 and 11.7% of the initial concentration entering the bioreactor, respectively. The selected mathematical model possessed a high predictive ability for the calculation of biotechnologies based on the anammox process

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.235

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 head0.006
GPT teacher head0.198
Teacher spread0.192 · 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

Citations13
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueApplied Biochemistry and MicrobiologySame topicWastewater Treatment and Nitrogen RemovalFrench-language works237,207