A mathematical approach of the entropic index applied to chemical systems
Bibliographic record
Abstract
Abstract This article deals with the mathematical efforts for developing an entropic performance index, taking into account both the different entropy concepts for establishing it, including Communication Theory, and its practical application. To illustrate the performance of the index developed, two different approaches were applied to a reactive system that consists of multiple generic reactions. The reactive system was optimized using the strategy of generating the minimum entropy rate and then the index was applied to check the progress of the process. The index increased from 0.2146 to 0.549, thus indicating a better result and, consequently, more favourable operating conditions. Additionally, a more refined procedure was carried out, which generated a maximum value for the index of 0.6174. To reveal the efficiency of this index, classic indicators based on trade‐offs between conversion and yield were also used to optimize the reactive system, which resulted in an index value of 0.6129. A detailed comparative analysis showed a convergence of the optimal regions, given by the classical method and that established by the entropy index. However, the optimal operating points are different, which can be explained by the interactions between the components considered by the entropic index. The conclusion to be drawn is that the results based on the entropy index describe the real system more appropriately, and therefore its performance is superior.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".