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
Advancement in electrophoresis and mass spectrometry techniques along with the recent progresses in genomics, culminating in bovine and pig genome sequencing, widened the potential application of proteomics in the field of veterinary medicine. The aim of the present review is to provide an in-depth perspective about the application of proteomics to animal disease pathogenesis, as well as its utilization in veterinary diagnostics. After an overview on the various proteomic techniques that are currently applied to veterinary sciences, the article focuses on proteomic approaches to animal disease pathogenesis. Included as well are recent achievements in immunoproteomics (ie, the identifications through proteomic techniques of antigen involved in immune response) and histoproteomics (ie, the application of proteomics in tissue processed for immunohistochemistry). Finally, the article focuses on clinical proteomics (ie, the application of proteomics to the identification of new biomarkers of animal diseases).
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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