MétaCan
Menu
Back to cohort
Record W1986199629 · doi:10.1021/jp910858z

Optimization of Parameters Used in Algorithms of Ion-Mobility Calculation for Conformational Analyses

2009· article· en· W1986199629 on OpenAlexaff
Chi‐Kit Siu, Yuzhu Guo, Irine S. Saminathan, Alan C. Hopkinson, K. W. Michael Siu

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2009
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsYork University
Fundersnot available
KeywordsIonProtonationChemistryAlgorithmComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.224

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.335
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations59
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry BSame topicMass Spectrometry Techniques and ApplicationsFrench-language works237,207