Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The skin harbours a diverse and abundant, yet inadequately investigated, microbial population. The population is believed to play an important role in both the pathophysiology and the prevention of disease, through a variety of poorly explored mechanisms. Early studies of the skin microbiota in dogs and cats reported a minimally diverse microbial composition of low overall abundance, most probably as a reflection of the limitations of testing methodology. Despite these limitations, it was clear that the bacterial population of the skin plays an important role in disease and in changes in response to both infectious and noninfectious diseases. Recent advances in technology are challenging some previous assumptions about the canine and feline skin microbiota and, with preliminary application of next-generation sequenced-based methods, it is apparent that the diversity and complexity of the canine skin microbiome has been greatly underestimated. A better understanding of this complex microbial population is critical for elucidation of the pathophysiology of various dermatological (and perhaps systemic) diseases and to develop novel ways to manipulate this microbial population to prevent or treat disease.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it