{"id":"W2910663748","doi":"10.1039/c8an02150c","title":"A parallelized molecular collision cross section package with optimized accuracy and efficiency","year":2019,"lang":"en","type":"article","venue":"The Analyst","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"Regional Municipality of Waterloo; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Collision; Section (typography); Cross section (physics); Computer science; Computational science; Parallel computing; Physics; Operating system","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":[0.001736502,0.001618125,0.001052047,0.000596391,0.0008511819,0.000974605,0.003547156,0.0008580143,0.02670689],"category_scores_gemma":[0.004476573,0.0007947168,0.0006751691,0.001381963,0.0003637948,0.001059789,0.00111829,0.002488107,0.00719185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001195086,"about_ca_system_score_gemma":0.002680782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00473884,"about_ca_topic_score_gemma":0.004612266,"domain_scores_codex":[0.9989537,0.0002081875,0.00007144545,0.0001584039,0.0005000735,0.0001082525],"domain_scores_gemma":[0.9984738,0.000508075,0.00009017636,0.0002434346,0.0006294877,0.00005489508],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002839349,0.0005587044,0.006147361,0.001768113,0.0007294284,0.0007375352,0.000949509,0.3160601,0.05811187,0.08361208,0.1735868,0.3548992],"study_design_scores_gemma":[0.0003937563,0.0001346387,0.00102068,0.0000316179,0.00006498221,0.0001629933,0.00003744184,0.8683716,0.0494525,0.008121287,0.07211363,0.00009483004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01961459,0.000285808,0.8863382,0.0003529958,0.000210777,0.0004718918,0.004185561,0.0761409,0.0123992],"genre_scores_gemma":[0.1321222,0.0003242613,0.8312556,0.0002967952,0.00007585373,0.002393309,0.005348936,0.01641767,0.0117653],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02670689,"threshold_uncertainty_score":0.08934349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006454397634153391,"score_gpt":0.2642207110110336,"score_spread":0.2577663133768802,"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."}}