{"id":"W2037683249","doi":"10.1006/jcis.2001.7652","title":"Genetic Algorithm Approach to the Determination of Particle Size Distributions from Static Light-Scattering Data","year":2001,"lang":"en","type":"article","venue":"Journal of Colloid and Interface Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inversion (geology); Light scattering; Range (aeronautics); Algorithm; Scattering; Genetic algorithm; Genetic data; Particle size; Static light scattering; Particle (ecology); Particle-size distribution; Experimental data; Inverse transform sampling; Optics; Computational physics; Statistical physics; Biological system; Materials science; Computer science; Physics; Mathematics; Statistics; Chemistry; Machine learning; Geology; Population; Biology","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":[0.001763411,0.0006867059,0.001031155,0.001358801,0.0007811394,0.0008605775,0.00158295,0.001534453,0.001007745],"category_scores_gemma":[0.004376133,0.0005492276,0.000762137,0.001168003,0.0008175798,0.0005681669,0.000643104,0.001135916,0.0002588318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001274041,"about_ca_system_score_gemma":0.001955482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01905976,"about_ca_topic_score_gemma":0.01079855,"domain_scores_codex":[0.9994853,0.000242988,0.00002966177,0.00008096948,0.0001243193,0.0000368567],"domain_scores_gemma":[0.9980901,0.001396808,0.00008176121,0.00005165829,0.0003419151,0.00003770308],"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":[0.00004871102,0.00007830757,0.0007488684,0.00003527479,0.00007902268,0.00004598989,0.00006523914,0.9118071,0.001387376,0.005347946,0.0004830771,0.07987309],"study_design_scores_gemma":[0.000006680559,0.000008857633,0.00007961034,0.000001744432,0.000005039313,0.00000804556,0.000003463228,0.9982292,0.0001970086,0.001348309,0.0001089384,0.000003049083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02383347,0.0001825657,0.9745663,0.0001684494,0.00002351845,0.00005341623,0.00002940818,0.0003662479,0.0007765681],"genre_scores_gemma":[0.3083703,0.0002455753,0.6880365,0.0001436625,0.0000595514,0.0003016719,0.0001781976,0.0001058905,0.002558596],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01905976,"threshold_uncertainty_score":0.03789765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02501380758383607,"score_gpt":0.3051298649196074,"score_spread":0.2801160573357713,"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."}}