Bibliographic record
Abstract
Cancer is the co-evolution of cancer cells and their turbulent microenvironment, characterized by dynamical hyper-chaotic gene expression profiles. However, cancers should not be viewed as the result of random mutations and malfunctioning information processing systems. Rather, it is the selective advantages conferred by adaptive evolution of cellular biosystems. Although on a systemic scale, cancer is defined as a disease, on a cellular basis they outperform healthy (non-transformed cells) in terms of survival and reproductive success. Their enhanced longevity pathways, metastatic invasion, extended telomeres, dynamical morphogenesis, regenerative stem cell division and environment-specific metabolic cascades indicate they are adaptive evolutionary cell states that have surpassed the boundaries normal cells are confined to. Therefore, the paper presents a brief summary of currently existing classical cancer models in the field of mathematical biology and the misconceptions of cancer epimetabolomes to further advance cancer research beyond its current limits. Through an insight into the mathematical behaviors of cancer cells, a quantum adaptive epigenetic landscape is proposed to explain the selective evolutionary dominance of cancer cells.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.030 |
| Scholarly communication | 0.005 | 0.015 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".