Resolving structure and function of metaorganisms through a holistic framework combining reductionist and integrative approaches
Bibliographic record
Abstract
Current research highlights the importance of associated microbes in contributing to the functioning, health, and even adaptation of their animal, plant, and fungal hosts. As such, we are witnessing a shift in research that moves away from focusing on the eukaryotic host sensu stricto to research into the complex conglomerate of the host and its associated microorganisms (i.e., microbial eukaryotes, archaea, bacteria, and viruses), the so-called metaorganism, as the biological entity. While recent research supports and encourages the adoption of such an integrative view, it must be understood that microorganisms are not involved in all host processes and not all associated microorganisms are functionally important. As such, our intention here is to provide a critical review and evaluation of perspectives and limitations relevant to studying organisms in a metaorganism framework and the functional toolbox available to do so. We note that marker gene-guided approaches that primarily characterize microbial diversity are a first step in delineating associated microbes but are not sufficient to establish proof of their functional relevance. More sophisticated tools and experiments are necessary to reveal the specific functions of associated microbes. This can be accomplished through the study of metaorganisms in less complex environments, the targeted manipulation of microbial associates, or work at the mechanistic level with the toolbox available in model systems. We conclude that the metaorganism framework is a powerful new concept to help provide answers to longstanding biological questions such as the evolution and ecology of organismal complexity and the importance of organismal symbioses to ecosystem functioning. The intricacy of the metaorganism requires a holistic framework combining reductionist and integrative approaches to resolve the structure and function of its member species and to disclose the various roles that microorganisms play in the biology of their hosts.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| 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".