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
Interest in digital radiography was stimulated by the enthusiastic acceptance of computed tomography in the early 1970s. It quickly became apparent to the medical community that images with improved information content, whose display characteristics could be manipulated by the viewer, provided many advantages. Subsequently, digital systems for subtraction angiography and later for conventional projection radiography and fluoroscopy were developed. The timing of the introduction of these systems was highly dependent on the readiness of certain key component technologies to meet the requirements of each of these applications. These components are the x-ray detectors, analog to digital converters, computers, data storage systems and high-resolution electronic displays and printers used in image acquisition, storage and display. Mammography represents one of the most demanding radiographic applications, simultaneously requiring excellent contrast sensitivity, high spatial resolution, and wide dynamic range at as low as radiation dose to the breast as is reasonably achievable while meeting the other requirements. For this reason, it is one of the last radiographic procedures to "go digital". Here, some of the considerations related to the detector technology for digital mammography will be discussed and systems currently available will be described.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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