A new mathematical method to describe the logic of the natural physical world
Bibliographic record
Abstract
To model an objective real world with absolute accurate mathematics is the symbol of modern science. However, in real life, the physical parameters could not be measured in infinite high accuracy, which makes the mathematical equations ineffective in the circumstance of turbulence and chaos. As the macro‐ and micro‐physical world could not be unified, it is believed that the laws of macro‐ and micro‐physical world are not the same. This paper proposes a new way of illustrating physical parameters, using the combination of mean ± error instead of a single numeric parameter for illustration, and establishes the corresponding algorithm and equations for means and errors, respectively. The results show that the error part of the Navier–Stokes equations in macro‐physical fluid mechanical world is just Schrodinger equation of micro‐physical quantum mechanical world. Therefore, a new method of researching complex system is proposed, and this method establishes the relationship between macro‐Newtown mechanics and micro‐quantum mechanics, so that in the macro world, the mean will follow the Navier–Stokes equations, and the error will follow the Schrodinger equations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".