Microbial co-occurrence network topological properties link with reactor parameters and reveal importance of low-abundance genera
Bibliographic record
Abstract
Abstract Operational factors and microbial interactions affect the ecology in anaerobic digestion systems. From 12 lab-scale reactors operated under distinct engineering conditions, bacterial communities were found driven by temperature, while archaeal communities by both temperature and substrate properties. Combining the bacterial and archaeal community clustering patterns led to five sample groups (ambient, mesophilic low-solid-substrate, mesophilic, mesophilic co-digestion and thermophilic) for co-occurrence network analysis. Network topological properties were associated with substrate characteristics and hydrolysis-methanogenesis balance. The hydrolysis efficiency correlated ( p < 0.05) with clustering coefficient positively and with normalized betweenness negatively. The influent particulate COD ratio and the relative differential hydrolysis-methanogenesis efficiency ( D efficiency ) correlated negatively with the average path length ( p < 0.05). Individual genera’s topological properties showed more connector genera in thermophilic network, representing stronger inter-module communication. Individual genera’s normalized degree and betweenness revealed that lower-abundance genera (as low as 0.1%) could perform central hub roles and communication roles, maintaining the stability and functionality of the microbial community.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".