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Record W2321421140 · doi:10.1021/je300601h

Reference Correlation for the Viscosity Surface of Hydrogen Sulfide

2012· article· en· W2321421140 on OpenAlexaff
Sergio E. Quiñones‐Cisneros, Kurt A. G. Schmidt, Binod Raj Giri, P. Blais, Robert A. Marriott

Bibliographic record

VenueJournal of Chemical & Engineering Data · 2012
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsViscosityChemistryThermodynamicsRange (aeronautics)Hydrogen sulfideWork (physics)Atmospheric temperature rangeExperimental dataStatisticsMaterials scienceOrganic chemistryPhysicsMathematicsComposite material

Abstract

fetched live from OpenAlex

Until recently, there was a substantial lack of reliable viscosity data for H 2 S, making the regression of an accurate H 2 S viscosity model significantly difficult. To derive a model for engineering applications (2008 H 2 S model), a corresponding states approach that related molecules of similar shape to H 2 S was applied to cover regions where no experimental data was available. Recently, new primary low-density experimental data and derived theoretical information have been published. Additionally, new high-pressure H 2 S viscosity measurements [at (373.15 and 423.15) K and up to 100 MPa] have also been reported. Based on this, a new revised correlation for the viscosity of H 2 S is presented in this work. The current correlation reproduces the primary H 2 S viscosity data to within experimental uncertainty. The precision of the new correlation varies from reference quality (better than ± 0.20 %) at low-densities to an estimated ± 5 % at 100 MPa and temperatures between (373 and 423) K. Outside this range of temperature there are no data to validate the accuracy of the model at elevated pressures; therefore we have conservatively estimated an uncertainty of roughly 10 % in the low-temperature and high-density region ( T < 323 K up to 100 MPa) and 5 % for the high-temperature region ( T > 450 K up to 100 MPa).

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score0.287

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 head0.032
GPT teacher head0.254
Teacher spread0.222 · 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 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

Citations8
Published2012
Admission routes1
Has abstractyes

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