{"id":"W2901191991","doi":"10.1016/bs.coac.2018.10.002","title":"Dynamic Clustering and Ion Microsolvation","year":2018,"lang":"en","type":"book-chapter","venue":"Comprehensive analytical chemistry","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Ion-mobility spectrometry; Chemistry; Ion; Context (archaeology); Mass spectrometry; Cluster analysis; Solvent; Chemical physics; Analytical Chemistry (journal); Chromatography; Organic chemistry; Computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0007630585,0.002043492,0.001374008,0.00163058,0.0009638907,0.001525098,0.002598856,0.001030926,0.02166351],"category_scores_gemma":[0.0006392953,0.0009282748,0.0007264644,0.002363838,0.0007433065,0.002364162,0.002289049,0.003006009,0.0271159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001024728,"about_ca_system_score_gemma":0.0009361477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008117615,"about_ca_topic_score_gemma":0.001832652,"domain_scores_codex":[0.9990484,0.00005881307,0.00002191317,0.0002742476,0.0005352686,0.00006135242],"domain_scores_gemma":[0.9997765,0.00005051374,0.00001357494,0.00005078598,0.00009109311,0.00001746912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001164473,0.0001158777,0.0001643325,0.001380689,0.00008112173,0.0001769221,0.0002175502,0.002557789,0.2066546,0.0504602,0.1369901,0.6010844],"study_design_scores_gemma":[0.000006761068,0.0000416956,0.0002939625,0.0001026227,0.00002732999,0.0005658612,0.00004239796,0.003078214,0.170513,0.01664388,0.8086259,0.00005824074],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006076729,0.08156764,0.7037439,0.00190262,0.003323936,0.0003549656,0.001657365,0.007052111,0.1943207],"genre_scores_gemma":[0.03489637,0.07972176,0.2924868,0.002894199,0.00130815,0.0008149461,0.006215536,0.005320203,0.576342],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02166351,"threshold_uncertainty_score":0.07247168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02042635095387356,"score_gpt":0.2696810524670626,"score_spread":0.2492547015131891,"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."}}