MétaCan
Menu
Back to cohort
Record W2080011262 · doi:10.1063/1.1359184

Racemic fluids of hard molecules

2001· article· en· W2080011262 on OpenAlexafffund
Jenel Vatamanu, N. M. Cann

Bibliographic record

VenueThe Journal of Chemical Physics · 2001
Typearticle
Languageen
FieldChemical Engineering
TopicThermodynamic properties of mixtures
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsClosure (psychology)Function (biology)Statistical physicsRange (aeronautics)MoleculeBridge (graph theory)PhysicsChemistryQuantum mechanicsMaterials science

Abstract

fetched live from OpenAlex

The structure in four racemic fluids is explored using two integral equation theories: the reference interaction site method (RISM) [D. Chandler and H. C. Andersen, J. Chem. Phys. 57, 1930 (1972)] and the diagrammatically correct theory of Chandler, Silbey, and Ladanyi (CSL) [D. Chandler, R. Silbey, and B. M. Ladanyi, Mol. Phys. 46, 1335 (1982)]. Discrimination is measured by comparison of site pair distributions for sites on identical molecules with the corresponding distributions for sites on mirror-image molecules. We find that discrimination is largest for distributions between the smallest sites in the molecules. Between racemates, those consisting of more asymmetrical chiral molecules (i.e., with a bigger range of site sizes and bond lengths) show the largest discrimination. The indirect correlation function is shown to be nondiscriminating in racemates. Further, exact relationships between like–like and like–unlike differences in the other pair functions have been obtained. From these, the importance of the bridge functions in discrimination is evident. The CSL theory always satisfies the exact relationships, even with approximate bridge diagrams. RISM theory cannot satisfy these exact limits regardless of density and closure relation. We have found that RISM theory predicts qualitatively incorrect pair distributions at low densities, but that the difference in the distributions is more accurate. All bridge diagrams which contribute to O(ρo) have been enumerated and evaluated. Inclusion of these diagrams into the CSL theory leads to exact results at low density. However, we find that the inclusion of the ρo diagrams has dramatically improved the quality of the CSL theory at all densities.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.216
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations9
Published2001
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Chemical PhysicsSame topicThermodynamic properties of mixturesFrench-language works237,207