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
Cancer is caused by accumulation of genetic changes which include activation of protooncogenes and loss of tumor suppressor genes. The age-specific incidence of cancer in general increases with advancing age. However, some cancers exhibit a bimodal distribution. Commonly recognized cancers with bimodal age distribution include acute lymphoblastic leukemia, osteosarcoma, Hodgkin's lymphoma, germ cell tumors and breast cancer. Delayed infection hypothesis has been used to provide explanation for the early childhood peak in leukemias and lymphomas, whereas the peak at an older age is associated with accumulation of protooncogenes and weakened immune system. Further genetic analysis and histopathological variations point to distinctly different cancers, varying genetically and histologically, which are often combined under a single category of cancers. Tumor characteristics and age distribution of these cancers varies also by population groups and has further implications on cancer screening methods. Although significant advances have been made to explain the bimodal nature of such cancers, the specific genetic mechanisms for each age distribution remain to be elucidated. Further distinction among the different cancer subtypes may lead to improvements in individual risk assessments, prevention and enhancement of treatment strategies.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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