Supplementary material to "A predictive viscosity model for aqueous electrolytes and mixed organic-inorganic aerosol phases"
Bibliographic record
Abstract
S1 Derivation for cation-anion viscosity contribution weightingThis section further describes the cation-anion contribution treatment introduced in Section 2.3 of the main text.For multi-ion mixtures, a special weighting must be derived to be fully consistent with all potential cation-anion pairings and such that there is no double counting of the contributions of a specific ion when paired up with the various anions.This can be accomplished by treating the aqueous solution as a mixture of (dissolved) charge-neutral cation-anion pairs, with each cation combined with each anion proportionally to the charge-weighted ion amounts involved in the solution overall.That is, we can think of the ions present in the solution as being the result of dissolving various possible electrolyte components (initially).The goal here is to provide a means of quantifying a "fair" share of each possible electrolyte component (as a binary, charge-balanced cationanion unit) in a clearly defined manner.Consider the total of positive charges in the aqueous electrolyte mixture, Jc c=1 n c • z c , which is equivalent in magnitude to the total of negative charges, Ja a=1 n a • |z a |, for an overall charge-neutral solution.We can
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.271 | 0.045 |
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".