{"id":"W3174309701","doi":"10.1039/d1an00557j","title":"Predicting differential ion mobility behaviour <i>in silico</i> using machine learning","year":2021,"lang":"en","type":"article","venue":"The Analyst","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Regional Municipality of Waterloo; National Institute for Nanotechnology; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada; Ontario Centres of Excellence","keywords":"In silico; Differential (mechanical device); Computer science; Machine learning; Biological system; Artificial intelligence; Chemistry; Physics; Biology; Thermodynamics; Biochemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001521541,0.0009178223,0.0005900285,0.0005099868,0.0002776477,0.0009209943,0.0009451075,0.0009926074,0.0009307541],"category_scores_gemma":[0.004322089,0.0002767554,0.0008149784,0.0004276102,0.0004288245,0.001038747,0.0005086637,0.001064291,0.0007325264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000699052,"about_ca_system_score_gemma":0.0008555016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002116217,"about_ca_topic_score_gemma":0.00288074,"domain_scores_codex":[0.9996316,0.00007016176,0.00002448679,0.0001347046,0.0001025769,0.00003641169],"domain_scores_gemma":[0.998571,0.0008464555,0.0001793821,0.0001435336,0.0002166977,0.00004290362],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005823487,0.0003937298,0.03297288,0.0007303551,0.0003401496,0.000339647,0.0002307985,0.5755295,0.2742575,0.003465434,0.003477889,0.1076798],"study_design_scores_gemma":[0.000008765569,0.0001350834,0.001807418,0.00001751071,0.00002623447,0.00006660152,0.00002536745,0.9379987,0.05722495,0.001370662,0.001290425,0.00002835152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.483149,0.0007638651,0.5057123,0.0005990508,0.00009613787,0.0001307997,0.00199229,0.004480674,0.003075834],"genre_scores_gemma":[0.8177813,0.0007242476,0.1773213,0.0003017025,0.00002541736,0.0001625781,0.002357736,0.000210863,0.001114905],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002116217,"threshold_uncertainty_score":0.008046806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01557016267830926,"score_gpt":0.2720506466815994,"score_spread":0.2564804840032902,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}