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Record W4414760845 · doi:10.1080/09593330.2025.2564904

Mechanistic insights into the sludge biodrying process: the evolution of sludge structure and microbial community

2025· article· en· W4414760845 on OpenAlexaff
Ning Li, Zhijian Li, Yajun Shi, Bing Zhu

Bibliographic record

VenueEnvironmental Technology · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicComposting and Vermicomposting Techniques
Canadian institutionsCarbon Engineering (Canada)
FundersAnhui Provincial Key Research and Development PlanNational Key Research and Development Program of ChinaNatural Science Foundation of Anhui Province
KeywordsDewateringAerationMoistureWater contentSewage sludgeMicrobial population biologyBiosolidsExtremophile

Abstract

fetched live from OpenAlex

This study deciphers sludge biodrying through a microbial-structural coevolution framework: Thermophilic consortia (Geobacillus/Bacillus; 63.27% abundance) drive bio-heat generation (>65°C), triggering particle fragmentation (decreased particle size by 63%), pore-network expansion (SEM-validated), and matrix loosening (decreased fractal dimension by 32%). This structural evolution enables phase-specific moisture redistribution - surface water (decreased from 68.52% to 19.82%) transforms into interstitial water (increased from 28.71% to 69.08%) and ultimately vapour flux, a process accelerated by capillary migration and enhanced airflow diffusion. The synergy of microbial succession (with dominance shifting from Firmicutes to Actinobacteria), structural reconfiguration, and moisture thermodynamics achieves deep dewatering (reducing moisture content from 80.62% to 41.62%), while mechanistic insights enable precision aeration phasing for energy reduction and cycle shortening via moisture-state-guided control.

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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.208
Teacher spread0.202 · 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 teacher head, 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

Citations0
Published2025
Admission routes1
Has abstractyes

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