{"id":"W2967780548","doi":"10.3233/jifs-179361","title":"Optimizing predictability of rating scales by differential evolution for the use by collective intelligent information and database systems","year":2019,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University","funders":"","keywords":"Predictability; Rating scale; Differential evolution; Computer science; Differential (mechanical device); Scale (ratio); Rating system; Data mining; Artificial intelligence; Machine learning; Statistics; Mathematics; Engineering","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":[0.003021708,0.000733468,0.00092851,0.0008731114,0.0004458024,0.001154924,0.0008568938,0.0005713726,0.0007318077],"category_scores_gemma":[0.01048655,0.0003524971,0.0006112566,0.0007390002,0.0004784261,0.0009653742,0.0009212801,0.0008755891,0.0001720058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000777119,"about_ca_system_score_gemma":0.0007960416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002097443,"about_ca_topic_score_gemma":0.001390708,"domain_scores_codex":[0.998118,0.0007104234,0.0001720025,0.0003541398,0.000529714,0.0001156674],"domain_scores_gemma":[0.9961823,0.002050797,0.0004344862,0.0003798151,0.0008589792,0.00009365228],"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":[0.0001365647,0.000193884,0.007937927,0.0001282975,0.0001513145,0.0001431536,0.0002492559,0.7035905,0.0121623,0.01068248,0.001040328,0.263584],"study_design_scores_gemma":[0.000005627402,0.00006360536,0.0008290744,0.000005005358,0.00001419941,0.00001911419,0.00001175514,0.9964676,0.0008210248,0.001475642,0.0002807645,0.000006494661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08352978,0.0002375744,0.9139146,0.0002186287,0.00003617334,0.00008806866,0.00002482523,0.0001881581,0.00176215],"genre_scores_gemma":[0.8481845,0.0001331033,0.1501525,0.00007369849,0.0000321758,0.0001815813,0.0001097245,0.00004053511,0.001092235],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003021708,"threshold_uncertainty_score":0.01598048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02114115788642999,"score_gpt":0.2456316753952977,"score_spread":0.2244905175088677,"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."}}