Hidden Information, Energy Dispersion and Disorder: Does Entropy Really Measure Disorder?
Bibliographic record
Abstract
Despite its appearance in physics around the 1850th, the second law of thermodynamics is still attracting more efforts to be clarified. More specifically, fifteen years later (1865) after its definition and introduction, entropy has been the subject of various interpretations. Hence, in physical sciences and notably in different education levels, its concept seems to be relatively tough to unambiguous decipher. In this work, we re-introduce the notion of entropy from classical, quantum and information theories viewpoints. The controversial over entropy and a measure of disorder misconception, stated by many scientists, is addressed as well to come up with less confusing physical interpretation of entropy. Hence, over time, an increase of entropy, a quantitative quantity, is most often associated to a rising of disorder, a non-quantitative quantity and no value-returning mathematical equation, rather than a continuously increasing of hidden data. In other words, linking disorder to hidden data is typically raising more confusion than clarification. Here, we shed more light on both concepts to find out an acceptable interpretation of entropy.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".