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 is growing appreciation that the current obesity epidemic is associated with increases in cancer incidence at a population level and may lead to poor cancer outcomes; concurrent decreases in cancer mortality at a population level may represent a paradox, i.e., they may also reflect improvements in the diagnosis and treatment of cancer that mask obesity effects. An association of obesity with cancer is biologically plausible because adipose tissue is biologically active, secreting estrogens, adipokines, and cytokines. In obesity, adipose tissue reprogramming may lead to insulin resistance, with or without diabetes, and it may contribute to cancer growth and progression locally or through systemic effects. Obesity-associated changes impact cancer in a complex fashion, potentially acting directly on cells through pathways, such as the phosphoinositide 3-kinase (PI3K) and Janus kinase-signal transducer and activator of transcription (JAK-STAT) pathways, or indirectly via changes in the tumor microenvironment. Approaches to obesity management are discussed, and the potential for pharmacologic interventions that target the obesity-cancer link is addressed.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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