Compost biodegradation of recalcitrant hoof keratin by bacteria and fungi
Bibliographic record
Abstract
AIMS: Compost activities efficiently break down a wide range of organic substances over time. In this study, bovine hoof was used as recalcitrant protein model to gain so far cryptic information on biodegradation during livestock mortalities composting. METHODS AND RESULTS: Bovine hooves (black and white), containing different amounts of melanin, placed into nylon bags were monitored during composting of cattle mortalities for up to 230 days. Besides physiochemical analysis, bacterial 16S and fungal 18S DNA fragments were amplified by PCR and profiles were separated by DGGE. Sequence analysis of separated fragments revealed various bacterial and fungal identities during composting. The microbial diversity was affected by a time-temperature interaction and by the hoof colour. Our molecular data, supported by electron microscopy, suggest hoof colonization by shifting bacteria and fungi communities. CONCLUSION: During composting, microbial communities work collaboratively in the degradation of recalcitrant organic matter such as keratin over time. SIGNIFICANCE AND IMPACT OF THE STUDY: A number of biomolecules including recalcitrant proteins may persist in environmental reservoirs, but breakdown can occur during composting. A combination of bioactivity and physiochemical conditions appear to be decisive for the fate of persistent biomolecules.
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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 | 0.000 | 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".