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
There are two broad classes (or categories) of excised human tissue: diagnostic tissue (DT) and research tissue (RT). Classification of excised human tissue does not define its ultimate use and ultimate use of excised human tissue does not define its classification. While both DT and RT can be used for research, DT has specific requirements with respect to how it must be handled if and when being accessed for research. We highlight distinguishing features of DT: (1) it is a clinical record, (2) it must be identifiable to a specific individual, (3) it is stewarded by pathology departments/clinical laboratories and (4) it has a mandatory retention period. We discuss how the further sub-classification of DT into archived DT (aDT) and excess DT (eDT) impacts the nature of its role in research. We examine the concept of DT as a clinical record and emphasize the impact of mandatory retention as it applies to how DT may be accessed for research purposes. We explain the role of post-retention eDT as a source of RT as well as procedures for access to in-retention aDT for research. Clarity of such issues will facilitate responsible access to DT for research.
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 | 0.025 | 0.263 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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