{"id":"W2804191529","doi":"10.1002/adts.201800069","title":"A Bayesian Approach to Predict Solubility Parameters","year":2018,"lang":"en","type":"article","venue":"Advanced Theory and Simulations","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Canadian Institute for Advanced Research","funders":"Solar Technologies go Hybrid; Deutsche Forschungsgemeinschaft; Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS); U.S. Department of Energy","keywords":"Solubility; Bayesian probability; Miscibility; Computer science; Flexibility (engineering); Probabilistic logic; Set (abstract data type); Toolbox; Consistency (knowledge bases); Biological system; Algorithm; Chemistry; Artificial intelligence; Polymer; Statistical physics; Mathematics; Physics; Organic chemistry","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":[0.003379322,0.0008392099,0.001241087,0.001796536,0.0007107222,0.001298927,0.001953046,0.001729777,0.003397763],"category_scores_gemma":[0.009061826,0.0009469674,0.001123823,0.00112669,0.00141106,0.002131333,0.001354558,0.001988841,0.0007558593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001271592,"about_ca_system_score_gemma":0.001793949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007162459,"about_ca_topic_score_gemma":0.004729291,"domain_scores_codex":[0.9987452,0.0005377494,0.00005668113,0.0002007996,0.0003577395,0.000101822],"domain_scores_gemma":[0.995878,0.00289896,0.0003491936,0.0002219415,0.0005259183,0.0001260291],"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":[0.00003862543,0.0000387881,0.0007488588,0.00004137399,0.00002895074,0.0000438142,0.0000268379,0.9481367,0.0007661686,0.03546545,0.0007935195,0.01387097],"study_design_scores_gemma":[0.000003526015,0.000005661665,0.00007492477,0.000005650656,0.000003193887,0.000006444383,0.000001931439,0.9889469,0.0001848528,0.01051741,0.0002436246,0.000006029617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01907603,0.0002897477,0.9772707,0.000390312,0.00003260553,0.00005202569,0.0002958385,0.0002864763,0.00230636],"genre_scores_gemma":[0.7601441,0.0008487449,0.2310808,0.0004362374,0.0002280407,0.0004085753,0.001089112,0.0002548744,0.005509456],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007162459,"threshold_uncertainty_score":0.01787174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01138128140926511,"score_gpt":0.2823534430196564,"score_spread":0.2709721616103913,"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."}}