An Advanced Platform for Thermodynamics Education. Part two: Monomer Quantum Volume in Pure Fluides
Bibliographic record
Abstract
The paper presents a remarkable application of the advanced thermodynamics education platform to the molar Gibbs energy G for basic particles in neat fluids. From the G, named also as the chemical potential, and the monomer fraction density Dm functions the Monomer Quantum Volume Vq (T) may be computed. The Vq (T) variable reflects the quantum uncertainty of basic particles positions in atomic and molecular fluids. It has proven to be universal for all fluid’s densities at a fixed temperature T. An extraordinary precision of modern thermophysical databases, such as the NIST Webbook, permits an estimation of the Monomer Quantum Volume values millions times lower than the atom’s volume! By studying the Vq (T) function the students can estimate the quantum uncertainty effects in pure fluids for a total range of their existence up to thousands of Kelvin! The enthalpy data normalization has proven to be very efficient. The advanced platform is highly useful and informative for thermodynamics education. For students it is very educative to study and utilize the author’s computer aided big thermophysical data analysis method to form their vision of the atomic and molecular quantum states distribution in pure fluids.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.088 | 0.044 |
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".