{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002961974,0.0000669962,0.0001430932,0.00004716359,0.0001291497,0.00009656995,0.0007066237,0.00001771412,0.00007675082],"category_scores_gemma":[0.0003900121,0.00004447782,0.00002481224,0.0006508746,0.0001506599,0.0002999601,0.0001746186,0.0000957099,0.000001598363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004808944,"about_ca_system_score_gemma":0.00007000106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002644797,"about_ca_topic_score_gemma":0.000003036468,"domain_scores_codex":[0.9990954,0.00001018241,0.0002930743,0.0001535609,0.0003009989,0.0001467844],"domain_scores_gemma":[0.9991673,0.0001208862,0.0002028862,0.0002752164,0.0001387206,0.00009494522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001300165,0.000081131,0.001261445,0.000007222112,0.00001518999,5.443179e-7,0.0004035359,0.00006337484,0.9933376,0.000005874335,0.0002135736,0.004597523],"study_design_scores_gemma":[0.000205793,0.00007283838,0.003974431,0.00003534139,0.00009256991,0.0000627398,0.001673495,0.03565093,0.9569882,0.00009903188,0.001064985,0.00007969666],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8325962,0.0004265702,0.1659863,0.0005441398,0.00004468817,0.00002607713,0.00003795801,0.000003079289,0.0003348861],"genre_scores_gemma":[0.9821656,0.00008585455,0.01749226,0.0000361095,0.00005994061,0.000001043619,6.801998e-7,0.000002881338,0.0001556485],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1495693,"threshold_uncertainty_score":0.1813753,"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."}}