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Record W4297163659

Anaerobic digestion of municipal solid waste: a mass balance analysis

2005· preprint· en· W4297163659 on OpenAlexaff
Vincent Vigneron, Laurent Mazéas, G. Barina, J.M. Audic, J.L. Vasel, Nicolas Bernet, Théodore Bouchez

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2005
Typepreprint
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsSuez (Canada)
Fundersnot available
KeywordsAnaerobic digestionMunicipal solid wasteWaste managementDigestion (alchemy)Balance (ability)Material balanceEnvironmental scienceChemistryProcess engineeringEngineeringChromatographyMedicineMethane
DOInot available

Abstract

fetched live from OpenAlex

Anaerobic digestion of two kinds of reconstituted municipal solid waste (MSW) was followed in laboratory scale experiments in order to obtain a carbon mass balance. Despite the complexity of the initial MSW matrix, triplicate reactors showed a reasonable level of reproducibility. Under water saturated conditions, recorded methane yields were important : 178 and 126 m3 of CH4/ton of dry waste I (mean French MSW composition) and II (compost replaced putrescibles), respectively. The carbon distribution at the end of the waste I degradation, without taking into account the carbon coming from the initial leachate, was : 30.7% ± 3.5% as mineralized fraction (TIC and CO2), 28.6% ± 2.8% as valorized fraction (CH4), 7.9% ± 2.5% as solubilized fraction (TOC) and 33.8% ± 3.8% as solid fraction. The carbon distribution for waste II was different : 22.7% ± 3.1% as mineralized fraction, 24.6% ± 1.1% as valorized fraction, 3.6% ± 2.0% as solubilized fraction and 49.1% ± 2.9% as solid fraction.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.232
Teacher spread0.221 · 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 designSimulation or modeling
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

Citations0
Published2005
Admission routes1
Has abstractyes

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