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Record W2114814867 · doi:10.1139/s07-015

Autothermal thermophilic aerobic digestion (ATAD) — Part I: Review of origins, design, and process operation

2007· article· en· W2114814867 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.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2007
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsPrecision Nanosystems (Canada)University of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Environmental Protection Agency
KeywordsAnaerobic digestionBiosolidsEnvironmental scienceWaste managementSewage sludgeThermophileProcess (computing)Biochemical engineeringSewage treatmentEngineeringComputer scienceEnvironmental engineeringChemistryBiologyEcologyMethane

Abstract

fetched live from OpenAlex

Increased legislative constraints have fuelled an interest in developing sustainable and economical methods for sludge digestion. Autothermal thermophilic aerobic digestion (ATAD) is a robust process that produces Class A biosolids from a wide range of organic sludge (e.g., animal waste, sewage sludge, food processing waste etc.). The advantages of this technology include good biomass biodegradation, pasteurization and process stability. Thermophilic temperatures result from the metabolic heat released by microorganisms during digestion. Efficient aeration and mixing are needed in addition to adequate reactor insulation to maintain thermophilic temperatures. Significant advances have been made in the optimization and adaptation of ATAD technology since it was first introduced in the early 1970s. Continuing innovation and advancement of the process is reflected in the number of patents for “next” generation technologies. Despite the apparent benefits of this process, ATAD is still not well understood. This article seeks to establish the existing state-of-the-art for the ATAD process. Information from a wide range of sources is presented to provide an insight into the key issues, discuss some of the advantages and perceived disadvantages, and list some of its operating limitations.

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)0.001
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 score0.217
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.007
GPT teacher head0.206
Teacher spread0.199 · 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