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Record W2981839501 · doi:10.1002/ceat.201900404

Effect of Substrate Characteristics and Process Fluid Percolation on Dry Anaerobic Digestion Processes

2019· article· en· W2981839501 on OpenAlexaff
Harald Wedwitschka, Daniela Gallegos, Michael Tietze, J. Reinhold, Earl Jenson, Jan Liebetrau, Michael Nelles

Bibliographic record

VenueChemical Engineering & Technology · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsAlberta Medical Association
FundersBundesministerium für Wirtschaft und Energie
KeywordsAnaerobic digestionCompactionMethaneMaterials sciencePercolation (cognitive psychology)Permeability (electromagnetism)Pulp and paper industryChemistryWaste managementChemical engineeringComposite materialOrganic chemistryEngineeringBiochemistry

Abstract

fetched live from OpenAlex

Abstract The dry anaerobic batch digestion process is an organic waste treatment technology most appropriate for the treatment of stackable (non‐free‐flowing) dry organic waste materials. The effect of the process fluid percolation and substrate permeability on methane production of organic household waste was investigated in anaerobic dry digestion trials at pilot scale. The container system consisted of two percolation digesters and a fixed‐bed methane digester. The experimental results indicate that material compaction occurs during the digestion process and can have a negative effect on substrate permeability. Structure material addition reduced material compaction and as a result increased the substrate permeability.

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)0.002
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.002
GPT teacher head0.190
Teacher spread0.188 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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