Factors Affecting the Diversity and Density of the Microbiomes
Bibliographic record
Abstract
Seven main factors are able to influence the development of microbiomes along with continuing to influence the density and diversity of these communities throughout the life of the host. Genetics may allow a pet to be more easily able to experience a disease state through the initiation or subsequent to perturbations of microbiomes. The nativity of microbiome establishment or age of the host can play a role with changes in function observed in the growth life stage of pets. Additionally, senior pets have been identified as having less diverse microbiomes altering immune function. The environment of the host and the GI tract can influence the type of microbiota that are able to live and complete physiological processes, including GI pH and barrier function. Stress plays a role on the health of the host and may influence multiple physiological systems, while nutrition can support the health of the host and can influence the growth of microbiota. Antibiotic therapy provides the most detrimental influence with changes in the microbiome, particularly the GI microbiome, having long-term effects and consequences.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".