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Record W4241811171 · doi:10.1021/je700078b

Predictive Correlation for <i>C</i><i><sub>p</sub></i> of Organic Solids Based on Elemental Composition

2007· article· en· W4241811171 on OpenAlexaff
Václav Laštovka, John M. Shaw

Bibliographic record

VenueJournal of Chemical & Engineering Data · 2007
Typearticle
Languageen
FieldChemistry
TopicChemical Thermodynamics and Molecular Structure
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsChemistryHeat capacityAsphalteneMoleculeSulfurOrganic moleculesAnalytical Chemistry (journal)ThermodynamicsNitrogenFusionElemental analysisChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

A correlation for the constant pressure heat capacity ( C p ) valid from 50 K to the fusion temperature for pure organic solids is presented. The predictive correlation includes seven universal coefficients. The variables are temperature and a parameter α, which possesses a value proportional to the number of atoms in a molecule divided by molar mass. This parameter is shown to be more robust than molecular structure or composition as a descriptor for C p . The training set comprises 72 organic solids with a wide range of molecular structures and elemental compositions including nitrogen-, oxygen-, and sulfur-substituted organic compounds. Solid−solid transition regions were excluded from the data set. The average absolute deviation for the comparison test set including 93 additional compounds with 2080 specific heat capacity values is less than 0.06 J·K -1 ·g -1 . The principal applications for the correlation are estimation of heat capacities for large organic molecules, where data are normally unavailable, and for poorly defined mixed organic solids such as asphaltenes where elemental analysis but not molecular structures are available.

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 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 score0.257
Threshold uncertainty score0.570

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 head0.006
GPT teacher head0.219
Teacher spread0.213 · 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.

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

Citations16
Published2007
Admission routes1
Has abstractyes

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