Bibliographic record
Abstract
Introduction If the world were made of pure substances, our development of the thermodynamic model would now be complete. We have developed a method, based on measurements of heat flow, that enables predictions to be made about which way reactions will go in given circumstances. But one of the reasons that the world is so complex is that pure substances are relatively rare, and strictly speaking they are nonexistent (even “pure” substances contain impurities in trace quantities). Most natural substances are composed of several components, and the result is called a solution . Therefore, we need to develop a way to deal with components in solution in the same way that we can now deal with pure substances – we have to be able to get numerical values for the Gibbs energies, enthalpies, and entropies of components in solutions. We will then be able to predict the outcome of reactions that take place entirely in solution, such as the ionization of acids and bases, and reactions that involve solids and gases as well as dissolved components, such as whether minerals will dissolve or precipitate. Our thermodynamic model will then be complete. In this chapter we have a look at how to deal with dissolved substances – solutes. When we mix two substances together, sometimes they dissolve into one another, like sugar into coffee or alcohol into water, and sometimes they do not, like oil and water.
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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 | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.061 | 0.016 |
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".