Optimization of Parameters Used in Algorithms of Ion-Mobility Calculation for Conformational Analyses
Bibliographic record
Abstract
Structural information of gaseous ions can be obtained by comparing their collision cross sections as determined by ion-mobility experiments with those by theoretical modeling. Three theoretical models, the projection approximation (PA), the exact hard-sphere scattering (EHSS), and the trajectory (TJ) models, have been employed to determine the theoretical cross sections of candidate geometries. The accuracy of these models is largely dependent on the empirical parameters used for ion-buffer gas interactions. Optimal empirical parameters for each model have been determined by comparing the experimental cross sections of 20 calibrant ions with their theoretical cross sections obtained by using geometries sampled by density-functional-theory-based molecular dynamics simulations. The maximum absolute deviations of the cross sections of 15.5% (PA), 20.7% (EHSS), and 11.7% (TJ) obtained from the original parameters are reduced to 5.6% (PA), 4.6% (EHSS), and 3.4% (TJ) obtained from the new optimized parameters. The root-mean-square deviations of the predicted cross sections using the new parameters from the experimental values are also drastically reduced to 2.1% (PA), 1.9% (EHSS), and 1.6% (TJ). The new parameters are verified on protonated triglycine, protonated trialanine, and doubly protonated bradykinin.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".