Bibliographic record
Abstract
Unlike equilibrium systems, which are characterized by a time-independent distribution of particles and energy, non-equilibrium systems experience constant fluxes of matter or energy, often due to external driving forces. These systems can exhibit a wide range of dynamic behaviors some of which can appear as self-organizing. Building on the framework of non-equilibrium statistical mechanics, Jeremy England in his paper "Statistical Physics of Adaptation" derives an equation which relates the relative forwards probability of transitioning between two possible macrostates with their respective reverse probability, entropy, and average energy dissipation across all possible micro trajectories. He showed that structures which are better at absorbing and dissipating energy into their surroundings during their formation have a higher likelihood of occurring, which can possibly explain how life-like behaviors emerge in non-equilibrium systems. In this project, we test England's equation on a toy model of self replicating populations, as well as attempt to apply it to a biological dataset. In the toy model under certain constraints we do find that our intuition of the systems dynamics does track with England's equation, that is we observe that the population with the greatest replication rate tends to have the highest energy dissipation when it's population makes up the greater fraction of the total population of particles. In the biological dataset we analyze protein-protein interactions and how the relative bonding energy and binding rates compare for different mutations. We explore the limitations of his equation when trying to make predictions about highly complex biological systems.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".