Bibliographic record
Abstract
The past few years have seen a tremendous interest in the potential of proteomics to address unmet needs in biomedicine. Such unmet needs include more effective strategies for early disease detection and monitoring and more effective therapies, in addition to developing a better understanding of disease pathogenesis. Proteomics is particularly suited for investigating biological fluids to identify disease-related alterations and to develop molecular signatures for disease processes. However, much of the effort undertaken in clinical proteomics to date represents either demonstrations of principles or relatively small-scale studies when compared with genomics effort and accomplishments or more pertinently when contrasted with the tremendous untapped potential of clinical proteomics. Clearly, we are in the early stages. What seems to be urgently needed is an organized effort to build a solid foundation for proteomics that includes developing a much needed infrastructure with adequate resources. The Human Proteome Organization (HUPO) is fostering an organized international effort in proteomics that includes initiatives around organ systems and biological fluids that have disease relevance as well as development of proteomics resources.
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 | 0.041 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.085 | 0.059 |
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".