Uncovering the Hidden Information: A Novel Approach to Modeling Physical Phenomena Through Information Theory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The growing need to study more complex physical phenomena and technological processes determines the importance of reducing the uncertainty of formulated models. However, measurement theory does not provide a clear answer to the question of how to calculate and use model structure uncertainty: the presence of certain base quantities and derived variables. The key novelty of this research lies in the informational method, which allows you to find the value of the uncertainty of the model of the phenomenon that has a certain structure. This uncertainty is initial and precedes the definition of uncertainties associated with the implemented computer algorithms, subsequent experiments, data processing, and the people involved in the study. This article aims to provide a detailed explanation of the informational method and its application for the selection of a model that satisfies the chosen universal criterion of comparative uncertainty. This criterion allows for solving the problem of identifying the preferred model that meets the requirements and philosophical outlook of the observer. So far, for many decades, no efforts have been made to take this uncertainty into account in scientific and technical practice. We applied the information method to analyze the attainable accuracy or perfection of established physical laws in this paper.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it