Thermodynamic principles for system biology and the patterns of flower pigmentation
Bibliographic record
Abstract
Abstract The thermodynamic principles for system biology are reviewed and formulated, and then basic patterns of flower pigmentation are interpreted. Main thoughts: 1) any biological trait (color or function of a cell) is logically related to a thermodynamic system (or physiological system, signaling network of the cell); 2) the striped, speckled and circle are three basic patterns of flower pigmentation, the development of flowers is an irreversible process;; 3) the patterns of flower pigmentation is formed in flower development; 4) the flower cells can change its color in a period of development and this process is controlled thermodynamically; 5) there is giant space of physiology within an organism and within it numerous thermal states can appear under different conditions. In this theory, the dominant inheritance means that a gene contributes great to the thermodynamic stability of a trait related system; different genes can be interacted or integrated thermodynamically according to their contribution to the stability of its related system. By combination of Turing theory and our views, complex patterns of pigmentation could be explained theoretically.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".