Bibliographic record
Abstract
The equine intestinal tract contains a complex microbial population (microbiota) that plays an important role in health and disease. Despite the undeniable importance of a 'normal' microbiota, understanding of the composition and function of this population is currently limited. As methods to characterize the microbiota and its genetic makeup (the microbiome) have evolved, the composition and complexity of this population are starting to be revealed. As is befitting a hindgut fermenter, members of the Firmicutes phylum appear to predominate, yet there are significant populations of numerous other phyla. The microbiome appears to be profoundly altered in certain disease states, and better understanding of these alterations may offer hope for novel preventive and therapeutic measures. The development and increasing availability of next generation sequencing and bioinformatics methods offer a revolution in microbiome evaluation and it is likely that significant advances will be made in the near future. Yet, proper use of these methods requires further study of basic aspects such as optimal testing protocols, the relationship of the fecal microbiome to more proximal locations where disease occurs, normal intra- and inter-horse variation, seasonal variation, and similar factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".