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Record W3159488164 · doi:10.1016/s0958-2118(21)00058-6

SUEZ's MABR technology helps UK facility meet strict ammonia targets within an existing plant footprint

2021· article· en· W3159488164 on OpenAlexaboutno aff
Simon Atkinson

Bibliographic record

VenueMembrane Technology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsnot available
Fundersnot available
KeywordsUpgradeFootprintEngineeringWaste managementCarbon footprintWastewaterEnvironmental engineeringComputer scienceGreenhouse gasGeographyOperating system

Abstract

fetched live from OpenAlex

SUEZ's ZeeLungTM technology has the ability to quickly, easily and cost-effectively upgrade existing wastewater treatment plants. As explained in an earlier issue of Membrane Technology (November 2020, page 5), it was recently used to upgrade a facility in Ontario, Canada. This focus article provides an insight into how the company's ZeeLung membrane aerated biofilm reactor technology is being used to help a facility in the UK meet stringent ammonia targets within an existing plant footprint.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.031
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0150.006

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 head0.231
Teacher spread0.211 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2021
Admission routes1
Has abstractyes

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