HOW CELLS AVOID ERRORS IN METABOLIC AND SIGNALING NETWORKS
Bibliographic record
Abstract
We examine features of intracellular networks that make errors less probable and beneficial responses more probable. In a false negative (F - ) error, a network does not respond to input. A network is reliable if it operates with a low probability of a F - error. Features that promote reliability include fewer reactions in sequence, more alternative pathways, no side reactions and negative feedback. In a false positive (F + ) error, a network produces output without input. Here, a network is specific if it has a low probability of a F + error. Conjunctions of signals within or between pathways can improve specificity through sigmoid steady-state response curves, kinetic proofreading and checkpoints. Both reliability and specificity are important in networks that regulate the fate of a cell and in networks with hubs or modules, and this includes scale-free networks. Some networks discriminate among several inputs by responding to each input through a different combination of pathways.
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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".