Fungal communities in biowaste composting: a comparative study of multiple ratios of woodchips vs. a perlite-cardboard blend as bulking agents
Bibliographic record
Abstract
The large production of biowaste can cause environmental risks if not managed properly. Composting is considered a sustainable solution for the disposal of this material, but generating high-quality compost requires proper design of feedstock composition and operational procedures. Microorganisms mediate the degradation of organic matter into a nutrient-rich substrate yet their response to different compost recipes is still poorly understood. In this study, fruit and vegetable biowaste was co-composted with woodchips or a perlite + cardboard mix in four different recipes (two representing optimal composting conditions and the other two were unideal composting conditions). Then, using Illumina high throughput sequencing, was defined the core fungal community shared by all treatments, mapped fungal succession through all composting phases and treatments, and identified indicator taxa associated to compost's specific recipes or phases. Our results confirm the presence of a core microbiome common to all compost piles dominated by Aspergillus but also reveal that every recipe has a degree of uniqueness in its fungal community and that the type of bulking agent used can significantly affect the composition of the fungal community during the latest stages of composting. At least one biomarker was associated with every recipe, with some reflecting the environmental conditions occurring such as the yeast P. kudriavzevii when more biowaste was present or T. lanuginosus when higher temperatures were reached during the thermophilic phase. These findings will contribute to increasing the knowledge of microbial dynamics during composting and provide useful information for the future use of biological parameters to assess compost quality.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".