Microbe-driven chemical ecology: past, present and future
Bibliographic record
Abstract
In recent years, research in the field of Microbial Ecology has revealed the tremendous diversity and complexity of microbial communities across different ecosystems. Microbes play a major role in ecosystem functioning and contribute to the health and fitness of higher organisms. Scientists are now facing many technological and methodological challenges in analyzing these complex natural microbial communities. The advances in analytical and omics techniques have shown that microbial communities are largely shaped by chemical interaction networks mediated by specialized (water-soluble and volatile) metabolites. However, studies concerning microbial chemical interactions need to consider biotic and abiotic factors on multidimensional levels, which require the development of new tools and approaches mimicking natural microbial habitats. In this review, we describe environmental factors affecting the production and transport of specialized metabolites. We evaluate their ecological functions and discuss approaches to address future challenges in microbial chemical ecology (MCE). We aim to emphasize that future developments in the field of MCE will need to include holistic studies involving organisms at all levels and to consider mechanisms underlying the interactions between viruses, micro-, and macro-organisms in their natural environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; both teacher heads agree on what is shown here.
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".