Retrospection with a New Purpose: Applying the Laplace Distribution to Chemical Engineering Problems
Bibliographic record
Abstract
Originating from the late eighteenth century and well known in contemporary probability theory, the Laplace distribution seems to have penetrated only into certain segments of scientific and engineering endeavors. This paper indicates its potential of representing adequately the random character of some phenomena of chemical engineering interest, and its capability of offering an admissible option vis-a-vis the traditional normal (Gaussian) distribution. A concise description of underlying theory is followed by quantitative illustrations taken from the textbook literature, where interpretation of material in terms of the normal distribution is prescribed. Nonetheless, choice of the level of significance in goodness-of-fit tests determines whether the Laplace distribution is, indeed, a viable alternative to the normal
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".