Model for Calculating the Density of Aqueous Electrolyte Solutions
Bibliographic record
Abstract
A new model for calculating the density of aqueous solutions of electrolytes has been developed. Parameters for 59 electrolytes were established on the basis of an extensive critical review of the published literature for solutions of one electrolyte in water, with over 10 700 points included. The average difference between the calculated and experimental density of solutions of water and one electrolyte is 0.10 kg m -3 with a standard deviation of 1.44 kg m -3 . The model was validated by predicting the density of systems of two or more electrolytes in water. The average difference between experimental and calculated values for over 1600 points is 0.003 kg m -3 with a standard deviation of 1.39 kg m -3 . The electrolytes studied are AlCl 3, Al 2 (SO 4 ) 3, BaCl 2, CaCl 2, CdCl 2, CdSO 4, CoCl 2, CoSO 4, CrCl 3, Cr 2 (SO 4 ) 3, CuCl 2, CuSO 4, FeCl 2, FeSO 4, FeCl 3, Fe 2 (SO 4 ) 3, HCl, HCN, HNO 3, H 3 PO 4, H 2 SO 4, KCl, K 2 CO 3, KNO 3, KOH, K 2 SO 4, LiCl, Li 2 SO 4, MgCl 2, MgSO 4, MnCl 2, MnSO 4, NaBr, NaCl, NaClO 3, Na 2 CO 3, NaF, NaHCO 3, NaH 2 PO 4, Na 2 HPO 4, NaHSO 3, NaI, Na 2 MoO 4, NaNO 2, NaNO 3, NaOH, Na 3 PO 4, Na 2 SO 3, Na 2 S 2 O 3, Na 2 SO 4, NH 3, NH 4 Cl, NH 4 NO 3, (NH 4 ) 2 SO 4, NiCl 2, NiSO 4, SrCl 2, ZnCl 2, and ZnSO 4 .
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".