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Record W3216278251 · doi:10.5541/ijot.980164

An Advanced Platform for Thermodynamics Education. Part two: Monomer Quantum Volume in Pure Fluides

2021· article· en· W3216278251 on OpenAlexfundno aff
Boris Sedunov

Bibliographic record

VenueInternational Journal of Thermodynamics · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum, superfluid, helium dynamics
Canadian institutionsnot available
FundersCanadian Institute for Theoretical Astrophysics
KeywordsThermodynamicsEnthalpyNISTQuantumMolar volumeStatistical physicsVolume (thermodynamics)MonomerQuantum chemistryStandard molar entropyChemistryPhysicsQuantum mechanicsComputer scienceMolecule

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.743
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.293
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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