Progress in Entropy Principle, as Disclosed by Nine Schools of Thermodynamics, and Its Ecological Implication
Bibliographic record
Abstract
The entropy principle has been commonly considered to be a selection principle. A history/philosophy-of-science analysis of development in thermodynamic thought was carried out based on a historical account of contributions to thermodynamics of nine Schools of thermodynamics plus that of Mayer/Joule (the Mayer-Joule principle), publication of A Treatise of Heat and Energy, development in maximum entropy production principle (MEPP), and process ecology formulated by Ulanowicz. The analysis discloses the dual nature in the entropy principle, as selection principle and causal principle, and that as well in thermodynamics: as equilibrium thermodynamics (Gibbsian thermodynamics) and as “engineering” thermodynamics in a general sense. Entropy-growth-potential (EGP) as the causal agent and the theory of engineering thermodynamics entail the concept of causal necessity, as suggested by Poincare. Recent development of the entropy principle into maximum entropy production principle (MEPP) is then critically analyzed. Special attention is paid to MEPP’s explanatory power of biological orders vs. that of process ecology: whereas MEPP asserts universal approach to physics and biology based on physical necessity and efficient causation, the case for “EGP as the causal agent and process ecology” allows biology to be different from physics by allowing the additional presupposition of causal necessity and efficacious causation.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".