How holobionts get sick—toward a unifying scheme of disease
Bibliographic record
Abstract
All humans, animals, and plants are holobionts. Holobionts comprise the host and a myriad of interacting microorganisms-the microbiota. The hologenome encompasses the genome of the host plus the composite of all microbial genomes (the microbiome). In health, there is a fine-tuned and resilient equilibrium within the members of the microbiota and between them and the host. This relative stability is maintained by a high level of microbial diversity, a delicate bio-geographic distribution of microorganisms, and a sophisticated and intricate molecular crosstalk among the multiple components of the holobiont. Pathobionts are temporarily benign microbes with the potential, under modified ecosystem conditions, to become key players in disease. Pathobionts may be endogenous, living for prolonged periods of time inside or on the host, or exogenous, invading the host during opportunistic situations. In both cases, the end result is the transformation of the beneficial microbiome into a health-perturbing pathobiome. We hypothesize that probably all diseases of holobionts, acute or chronic, infectious or non-infectious, and regional or systemic, are characterized by a perturbation of the healthy microbiome into a diseased pathobiome.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.019 | 0.030 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".