A probabilistic model to identify the core microbial community
Bibliographic record
Abstract
ABSTRACT The core microbial community has been hypothesized to have essential functions ranging from maintaining health in animals to protection against plant disease. However, the identification of the core microbial community is frequently based on arbitrary thresholds, selecting only the most abundant microorganisms. Here, we developed and tested an approach to identify the core community based on a probabilistic model. The Poisson distribution was used to identify OTUs with a probable occurrence in every sample of a given dataset. We identified the core communities of four extensive microbial datasets, and compared the results with conventional, but arbitrary, methods. The datasets were composed of the microbiomes of humans (tongue, gut, and skin), mice (gut), plant (grapevine) tissue, and the maize rhizosphere. Our proposed method revealed core microbial communities with higher richness and diversity than those previously described. This method also includes a greater number of rare taxa in the core, which are often neglected by arbitrary threshold methods. We demonstrated that our proposed method revels a probable core microbial community for each different habitat, which extend our knowledge about shared microbial communities. Our proposed method may help the next steps proving the essential functions of core microbial communities. Originality-Signifìcance Statement More rigorous and less arbitrary statistical methods could increase knowledge regarding the role of microorganisms and their interactions. Here, we suggest a probabilistic method to identify the microbial core community across systems. Our method identifies a large proportion of the rare community that likely belongs to the microbial core community, which was not identified by conventional methods. Our probabilistic model is a non-arbitrary approach to defining the microbial core community, which may help in the next step of the microbial core community studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".