65 Metagenomic analysis of ensiled grocery food waste
Bibliographic record
Abstract
Abstract Vegetables and fruits are the main sources of plant fiber, minerals, and vitamins essential for human health. However, commercially manufactured foods which are not consumed end up in landfills and contribute to economic losses as well as to methane emissions, a global warming gas. Besides recycling food waste (FW) as compost to reduce unsaleable vegetables and fruits intended for landfill, it can be ensiled as an alternative source for a sustainable livestock feed. Metagenomics is the analysis of genetic material purified directly from environmental samples by sequencing and enables taxonomic identification of the microbial communities present. This study aimed to evaluate the silage produced with different mixtures (fruit, vegetables, bread, and bakery products) of FW after 60 d to evaluate microbial community and functional diversity. The FW was ground to a particle size of 1 cm2 and three treatments in duplicates were obtained: control (refrigerated), sundried (exposed to sun) and passive-dried (inside the building). The DNA of the ensiled FW was extracted for two metagenomic sequencing approaches; specific regions sequencing including 16S rRNA (bacterial diversity), 18S rRNA (species differences in eukaryotes), internal transcribed spacer (ITS; for fungal classification) and shotgun sequencing. Specific coding region sequencing is more precise while shotgun sequencing can better identify the most abundant organisms. Therefore, we are using these two methods for comparative analysis and to enable us to capture the entire microbial community. Analysis of the metagenomic data are ongoing with the intention to characterize potential pathogenic bacteria and antimicrobial resistance genes within the ensiled FW to confirm that the ensiled FW is safe to feed to cattle. We are awaiting data for specific region sequencing. Overall, we expect to confirm if this approach is useful for silage analysis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".