The role of disparity-mutagenesis model on tumor development with special reference to increased mutation rate
Bibliographic record
Abstract
Human solid tumors are believed to have very high mutation rates at least in the early stage of extension. Irrespective ofwhether the increased mutation rate is a necessary condition for the tumor development or not, extremely high mutationrates such as in excess of the so-called “threshold” would before long result in the natural death of tumor cells. In reality,however, we are dying by cancer. Thus, it has been hypothesized that tumor cells should make a quick transition from thehigher mutation state to the lower one. According to our “disparity-mutagenesis model”, however, carcinogenesis couldcontinue without any incident even under a prolonged period of high mutation rates. Namely, if lagging-strand-biasedmutations far beyond the threshold of mutation rate are introduced in tumor cells, the tumor could progress to malignantextension without extinction. The results of evolution experiments using mutator microorganisms are discussed in terms ofcarcinogenesis.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".