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Record W2625279425 · doi:10.1080/17597269.2017.1336348

Nutrient removal and recovery from digestate: a review of the technology

2017· review· en· W2625279425 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBiofuels · 2017
Typereview
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsCentre National en Électrochimie et en Technologies EnvironnementalesCollège Shawinigan
Fundersnot available
KeywordsDigestateAnaerobic digestionProcess (computing)Environmental scienceWaste managementProcess engineeringComputer scienceEngineeringChemistry

Abstract

fetched live from OpenAlex

Digestate is a byproduct of anaerobic digestion, which can be considered waste or a product of potential use for the chemical industry or agriculture. In either case, the digestate must usually be treated prior to being disposed of or valorized. This review describes digestate processing technologies and their specific characteristics. Nutrient recovery and removal from digestate can be achieved through mechanical, physicochemical or biological processes. Available and potential digestate treatment techniques are presented. The complexities of the technologies available, legislation, the agronomical value of the digestate and the economic value of the process mean a decision support tool is required to help managers choose the best digestate processing technology. To ensure adequate analysis, the whole biomethanization project should be integrated in the use of these decision support tools. The objectives and limits of some of the currently available tools are analyzed at the end of this review.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.034
GPT teacher head0.294
Teacher spread0.260 · 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