Mechanistic insights into the sludge biodrying process: the evolution of sludge structure and microbial community
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".