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
The integration of evolutionary and developmental approaches into the field of evolutionary developmental biology has opened new areas of inquiry- from understanding the evolution of development and its underlying genetic and molecular mechanisms to addressing the role of development in evolution. For the last several decades, the terms 'evolution' and 'development' have been increasingly linked to cancer, in many different frameworks and contexts. This mini-review, as part of a special issue on Evolutionary Developmental Biology, discusses the main areas in cancer research that have been addressed through the lenses of both evolutionary and developmental biology, though not always fully or explicitly integrated in an evo-devo framework. First, it briefly introduces the current views on carcinogenesis that invoke evolutionary and/or developmental perspectives. Then, it discusses the main mechanisms proposed to have specifically evolved to suppress cancer during the evolution of multicellularity. Lastly, it considers whether the evolution of multicellularity and development was shaped by the threat of cancer (a cancer-evo-devo perspective), and/or whether the evolution of developmental programs and life history traits can shape cancer resistance/risk in various lineages (an evo-devo-cancer perspective). A proper evolutionary developmental framework for cancer, both as a disease and in terms of its natural history (in the context of the evolution of multicellularity and development as well as life history traits), could bridge the currently disparate evolutionary and developmental perspectives and uncover aspects that will provide new insights for cancer prevention and treatment.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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