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Record W2761808020 · doi:10.2166/wst.2017.511

Simultaneous organic matter removal and nitrification of an inert self-supporting immersed media to upgrade aerated lagoons

2017· article· en· W2761808020 on OpenAlexaff
Étienne Boutet, Serge Baillargeon, Bernard Patry, Paul Lessard

Bibliographic record

VenueWater Science & Technology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsNitrificationEffluentAerationOrganic matterChemistryInertPulp and paper industryEnvironmental chemistryEnvironmental engineeringAerated lagoonEnvironmental scienceWastewaterNitrogenActivated sludgeOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Abstract A pilot study was performed to evaluate the potential of an inert self-supported immersed fixed film media to upgrade aerated lagoons. Simultaneous organic matter removal and nitrification was assessed under different loading rates and temperatures (near 0 °C) using 12 laboratory-scale reactors operated in parallel. Test results showed that both the temperature and the load have an influence on organic matter effluent concentrations. Effluent quality seemed related to the observed biofilm thickness. Thicker biofilm is believed to have contributed to biomass detachment and increased particulate organic matter concentrations in the effluent. Simultaneous organic removal and nitrification was obtained at loads above 5 g CBOD5/m2·d. The highest nitrification rate at 0.4 °C was obtained for the smallest load, which showed a nitrification limitation close to freezing point.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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 head0.008
GPT teacher head0.238
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2017
Admission routes1
Has abstractyes

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