Physiological traits and relative abundance of species as explanatory variables of co-occurrence pattern of cultivable bacteria associated with chia seeds
Bibliographic record
Abstract
Deciphering the rules defining microbial community assemblage is envisioned as a promising strategy to improve predictions of pathogens colonization and proliferation in food. Despite the increasing number of studies reporting microbial co-occurrence patterns, only a few attempts have been made to challenge them in experimental or theoretical frameworks. Here, we tested the hypothesis that observed variations in co-occurrence patterns can be explained by taxonomy, relative abundance, and physiological traits of microbial species. We used PCR amplicon sequencing of taxonomic markers to assess distribution and co-occurrence patterns of bacterial and fungal species found in 25 chia (Salvia hispanica L.) samples originating from eight different sources. The use of nutrient-rich and oligotrophic media enabled isolation of 71 strains encompassing 16 bacterial species, of which five corresponded to phylotypes represented in the molecular survey. Tolerance to different growth inhibitors and antibiotics was tested to assess the physiological traits of these isolates. Divergence of physiological traits and relative abundance of each pair of species explained 69% of the co-occurrence profile displayed by cultivable bacterial phylotypes in chia. Validation of this ecological network conceptualization approach to more food products is required to integrate microbial species co-occurrence patterns in predictive microbiology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".