A generalized linear secant bulk modulus based correlation for the prediction of compressed liquid density
Bibliographic record
Abstract
Abstract A generalized correlation for the prediction of compressed liquid density has been developed based on the linear secant bulk modulus (LSM) concept. The correlation was developed using a dataset containing over 25 000 density data points from non‐polar and polar components collected from the literature. The LSM correlation fitted the data in this dataset with an overall average absolute deviation of 6 kg/m 3 . The developed correlation applies to pure components at reduced temperatures up to 0.99 and pressures up to 1 × 10 6 kPa and requires the following inputs: critical properties, acentric factor, saturated liquid density, and saturation pressure. The developed LSM correlation was tested on predicting the density of 18 components (over 23 000 data points) from assorted chemical families, including alcohols. The overall average absolute deviation was 5 kg/m 3 . For comparison, the Chang–Zhao correlation predicted the densities in the same Test Dataset with an overall average absolute deviation of 6 kg/m 3 . However, at pressures above 1 × 10 5 kPa, the LSM correlation predicted more accurate densities with an overall average absolute deviation of 11 kg/m 3 , whereas the Chang–Zhao correlation predicted the densities with an overall average absolute deviation of 26 kg/m 3 .
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".