High-throughput 16S rRNA gene sequencing of the microbial community associated with palm oil mill effluents of two oil processing systems
Bibliographic record
Abstract
Palm Oil Mill Effluents (POME) are complex fermentative substrates which habour diverse native microbial contaminants. However, knowledge on the microbiota community shift caused by the anthropogenic effects of POME in the environment is up to date still to be extensively documented. In this study, the bacterial and archaeal communities of POME from two palm oil processing systems (artisanal and industrial) were investigated by Illumina MiSeq Platform. Despite the common characteristics of these wastewaters, we found that their microbial communities were significantly different with regard to their diversity and relative abundance of their different Amplicon Sequence Variants (ASV). Indeed, POME from industrial plants harboured as dominant phyla Firmicutes (46.24%), Bacteroidetes (34.19%), Proteobacteria (15.11%), with the particular presence of Spirochaetes, verrucomicrobia and Synergistetes, while those from artisanal production were colonized by Firmicutes (92.06%), Proteobacteria (4.21%) and Actinobacteria (2.09%). Furthermore, 43 AVSs of archaea were detected only in POME from industrial plants and assigned to Crenarchaeota, Diapherotrites, Euryarchaeota and Nanoarchaeaeota phyla, populated mainly by many methane-forming archaea. Definitively, the microbial community composition of POME from both type of processing was markedly different, showing that the history of these ecosystems and various processing conditions have a great impact on each microbial community structure and diversity. By improving knowledge about this microbiome, the results also provide insight into the potential microbial contaminants of soils and rivers receiving these wastewaters.
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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) | 0.000 | 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) | 0.000 | 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".