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 concept of reference values is widely accepted, but their application has been quite lax over the years. This is due primarily to the difficulty of properly selecting and documenting samples from a reference population. In the absence of a clear description of reference individuals, reference values lose their meaning, are ambiguous at best, and are often confused with decision limits. The clinical medicine perspective of reference values is to rule out diseases and to define health, while that of preventive medicine is to appreciate the state of health. Defining reference limits and normality in this context is a great challenge. Advances in the fields of genomics and proteomics and the rapid pace of technological advances help highlight the biological diversity among individuals. However, there is a great need for reference values that are representative of healthy humans and presented in a manner that they can be utilized by all laboratories. In addition, as secure information technology becomes available, the goal of using an individual as their own reference during a lifetime is now possible, provided that consistency of databases is ensured.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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