Principal component analysis of solvent effects on equilibria and kinetics — A hemisphere model
Bibliographic record
Abstract
Principal Component Analysis (PCA) was applied to 17 selected properties relating to the properties of solvents and their effect on solute spectra and equilibria and rates of reactions in solution. The analysis showed affinities and contrasts among the parameters. Using the first three principal components (PC's) as unit vectors in the Cartesian directions, the properties were represented as vectors in this space with their directions being represented as points on the unit hemisphere; their magnitudes are lost in the process of correlation. Properties that fell into recognizable groups included the following: (i) those purporting to measure acidity (A) (α, AN, SA, and Aj), closely associated with ETN, Z, S, and A(14N), which are usually considered to be measures of polarity; (ii) properties supposed to measure basicity (B) (β, SB, and DN); (iii) polarity group (P), including pure (di-)polarity properties (βµ1/2 and Qv) and those measuring some combinations of polarity and polarizability (π*, πazo, SPP, and –χR). The space spanned by the three PCs of the properties provided a frame in which solvent effects on equilibria and on rates of reactions could be represented. Eight equilibria, represented by log10(K) measured in different solvents, and 10 reaction rates, similarly represented by log10(k), were correlated with the three PCs of the solvent properties. This enabled plotting of their directions in the hemisphere to show which groups of properties were most strongly correlated with the rate of each reaction. A separate plot showed the magnitudes and sense of the effects. These finding are considered in the light of the mechanisms proposed for the reactions.Key words: Chemometrics, Principal Component Analysis, solvent effects on equilibria and rates.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".