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Record W2018997203 · doi:10.1063/1.1541615

On the first-order mean spherical approximation

2003· article· en· W2018997203 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Chemical Physics · 2003
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsHoneywell (Canada)
Fundersnot available
KeywordsYukawa potentialHard spheresFactorizationSimple (philosophy)SPHERESOrder (exchange)Statistical physicsCritical point (mathematics)Phase (matter)MathematicsPhysicsMathematical analysisMathematical physicsQuantum mechanics

Abstract

fetched live from OpenAlex

The general solution of the Ornstein–Zernike equation presented by Tang and Lu [J. Chem. Phys. 99, 9828 (1993)] is further discussed. By applying the Hilbert transform, the first-order factorization and direct correlation functions (DCF) are generally and analytically obtained, with emphasis on the mean spherical approximation (MSA) for Yukawa fluids. These analytical results are employed to produce a new DCF for hard spheres through integrating with the previous generalized mean spherical approximation [J. Chem. Phys. 103, 7463 (1995)]. The new DCF is of simple analytical form and remedies the deficiencies of its Percus–Yevick version at high densities. Comparisons between the first-order and full MSA solutions are also made. It is shown that the two solutions give very close results for thermodynamic properties in the phase stable region and phase coexistence curves away from the critical point. At unstable states, the first-order MSA looks more advantageous when applications go beyond homogeneous.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score0.193

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 communication0.0000.000
Open science0.0000.000
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 head0.201
Teacher spread0.191 · 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