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Record W2025904535 · doi:10.1089/ees.2005.0091

A Temperature-Guided Three-Stage Inoculation Method for Municipal Solid Wastes Composting

2007· article· en· W2025904535 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

VenueEnvironmental Engineering Science · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicComposting and Vermicomposting Techniques
Canadian institutionsUniversity of Regina
FundersNational Key Research and Development Program of China
KeywordsMicroorganismInoculationPopulationOdorEnvironmental sciencePulp and paper industryWaste managementProcess (computing)ChemistryBiologyBacteriaComputer scienceEngineeringHorticultureMedicine

Abstract

fetched live from OpenAlex

Inoculation is a human-induced measure that can significantly enhance the composting process by increasing the initial microbial population, formulating viable microbial communities, and generating desired enzymes. However, the inoculation efficiency was usually subjected to competitions of indigenous microorganisms. In this study, a temperature-guided three-stage inoculation (TGTSI) method was developed to control the indigenous cell concentrations, and in turn, enhance the composting efficiency. The experimental results indicated that the TGTSI method could effectively suppress the initial cell concentrations of indigenous microorganisms, which resulted in significantly enhancement of biodegradations as well as the reduction of odor emissions. Moreover, a two-stage kinetics method was employed to analyze the TGTSI mechanisms. The model results were consistent with the experimental data. In conclusion, this study implied that TGTSI could not only enhance the composting process but also save system cost.

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 score0.462
Threshold uncertainty score0.376

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 head0.022
GPT teacher head0.276
Teacher spread0.254 · 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