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Record W2959285647 · doi:10.1038/s41545-019-0038-x

Towards Quality by Design and process analytical technology for enhanced nutrient recovery from wastewaters

2019· article· en· W2959285647 on OpenAlexafffund
Céline Vaneeckhaute

Bibliographic record

Venuenpj Clean Water · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsQuality (philosophy)Resource recoveryProcess (computing)Resource (disambiguation)Consistency (knowledge bases)Computer scienceRisk analysis (engineering)Key (lock)Environmental economicsBiochemical engineeringWastewaterWaste managementBusinessEngineering

Abstract

fetched live from OpenAlex

Abstract Recovering nutrients from wastewater as sustainable bio-based products provides a key solution to major environmental problems. Classical technology development approaches for resource recovery largely ignore the real-world variability in raw waste materials, which currently hinders the successful implementation of recovery strategies. A major challenge is to create a consistent and environmentally friendly supply of high-quality end-products that can compete with fossil-derived products currently on the market. This paper suggests the use of a Quality by Design approach as adapted from the pharmaceutical industry to ensure a high standard of quality consistency. Key elements of this approach involve mathematical models and integrated design-control strategies that support the production of high-quality marketable end-products from variable input waste and wastewater streams. Further research in terms of cost evaluation and optimisation of such approach for resource recovery applications is needed. A regulatory framework for Process Analytical Technology implementation in the field is also suggested.

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 score0.211
Threshold uncertainty score0.815

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)0.0010.000

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 head0.011
GPT teacher head0.246
Teacher spread0.234 · 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

Citations4
Published2019
Admission routes2
Has abstractyes

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