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
Whenever we create a test to screen for a disease, to detect an abnormality or to measure a physiological parameter such as blood pressure (BP), we must determine how valid that test is—does it measure what it sets out to measure accurately? There are lots of factors that combine to describe how valid a test is: sensitivity and specificity are two such factors. We often think of sensitivity and specificity as being ways to indicate the accuracy of the test or measure. In the clinical setting, screening is used to decide which patients are more likely to have a condition. There is often a ‘gold-standard’ screening test—one that is considered the best to use because it is the most accurate. The gold standard test, when compared with other options, is most likely to correctly identify people with the disease (it is specific), and correctly identify those who do not have the disease (it is sensitive). When a test has a sensitivity of 0.8 or 80% it can correctly identify 80% of people who have the disease, but it misses 20%. This smaller group of people have the disease, but the test failed to detect them—this is known as a false negative. A test that has an 80% specificity can correctly identify 80% of people in a group that do not have a disease, but it will misidentify 20% of people. That group of 20% will be identified as having the disease when they do not, this is known as a false positive. See box 1 for definitions of common terms used when describing sensitivity and specificity. Box 1 ### Common terms Sensitivity: the ability of a test to correctly identify patients with a disease. Specificity: the ability of a test to correctly identify people without the disease. True positive: the person has the disease and …
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 | 0.087 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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