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Microwave Effects on Soluble Substrate and Thermophilic Digestibility of Activated Sludge

2013· article· en· W1458869553 on OpenAlexaff
Nuno Miguel Gabriel Coelho, Ronald L. Droste, Kevin J. Kennedy

Bibliographic record

VenueWater Environment Research · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMagnetic and Electromagnetic Effects
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsActivated sludgeThermophileSubstrate (aquarium)ChemistryMicrowavePulp and paper industryFood scienceWaste managementChemical engineeringEnvironmental scienceBiochemistryBiologyEnvironmental engineeringEnzymeSewage treatmentEcologyEngineering

Abstract

fetched live from OpenAlex

Thickened waste-activated sludge (TWAS) was subjected to microwave pretreatment and athermal irradiation. The soluble phase of each type of TWAS pretreatment was subject to ultrafiltration in series using progressively smaller pore-size membranes (300, 100, 10, and 1 kDa) and biodegradability tests. Microwave pretreatment solubilizes a considerable amount of the suspended organic substrate, but athermal irradiation also causes solubilization of organic matter, although at a smaller scale than microwave. Proteins are particularly sensitive to athermal irradiation, and both microwave and athermal irradiation are capable of changing the size distribution of dissolved organic matter. Athermal irradiation and microwave have a substantially different effect on thermophilic anaerobic biodegradability of the various size fractions obtained after ultrafiltration. Slight inhibition and decrease in total biogas production was measured in some microwave tests. Athermal irradiation does not cause a decrease in maximum biogas production rate in any test and increases slightly biogas production.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.013
GPT teacher head0.249
Teacher spread0.236 · 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

Citations16
Published2013
Admission routes1
Has abstractyes

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