Reference Correlation for the Viscosity Surface of Hydrogen Sulfide
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".