MétaCan
Menu
Back to cohort
Record W3156461085 · doi:10.22215/etd/2014-10482

Evaluation of Microalgae for Secondary and Tertiary Wastewater Treatment

2014· dissertation· en· W3156461085 on OpenAlexaff
Zainab Abdulsada

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnergy
TopicAlgal biology and biofuel production
Canadian institutionsCarleton University
Fundersnot available
KeywordsEffluentWastewaterSewage treatmentPhosphorusActivated sludgeSecondary treatmentNutrientChemistryPulp and paper industryEnvironmental chemistryEnvironmental engineeringEnvironmental scienceOrganic chemistry

Abstract

fetched live from OpenAlex

In this study, use of microalgae for secondary and tertiary wastewater treatment was evaluated.First phase of the study investigated the ability of microalgae to remove nutrients, organic carbon and indicator bacteria from secondary effluents and centrate.For secondary wastewater and centrate, the reductions in soluble concentrations of total nitrogen, phosphorus, and COD were 27, 51.7, 29.5% and 49.4,78.6.32.8%, respectively.Total coliform reduction was greater than 99.5%.The second phase investigated the use of microalgae in combination with activated sludge system.Soluble COD removal improved from 1.5% for sample A (activated sludge) to 65.6 and 77.8% for samples B (activated sludge and microalgae) and C (microalgae).Ammonia was removed by 99.9% for B and C, while the removal was 46.4% for A. Total dissolved phosphorus was removed by 81.3 and 73.2% for B and C, but there was no reduction in dissolved phosphorus for A. 5.3.1Biomass growth and monitoring ................

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)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.020
GPT teacher head0.277
Teacher spread0.257 · 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

Citations14
Published2014
Admission routes1
Has abstractyes

Explore more

Same topicAlgal biology and biofuel productionFrench-language works237,207