How biological cells including platelets and megakaryocytes decide complex problems fast but risky
Bibliographic record
Abstract
Short Abstract Mathematical decision processes are accurate but sometimes take very long time or simply do not happen. Decisions in biology happen fast and driven by evolution, optimizing survival chances. This results in stochastic decisions with on average good adaptation to the environment but an inherent risk of individual errors e.g. developing cancer during cell regeneration. We calculate and show in platelets and megakaryocytes how cellular decision processes increases risk for errors and inflammation. Short cut solutions adapted from nature improve computer strategies for protein folding and network decision processes. Complex problems are not always solved in foreseeable time, instead the fast solutions in biology speed up errors everywhere including biochemical aging of blood vessels, misfolded proteins, mis-programmed cells, cancer and heart failure. One sentence abstract We investigate in biological networks how complex decision problems are mastered not by an accurate but unforeseeable long mathematical search but rather pragmatic and fast, with an inherent risk of error, a basis for inflammation and cancer.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".