{"id":"W4232212579","doi":"10.26434/chemrxiv.6189617","title":"A Bayesian Approach to Predict Solubility Parameters","year":2018,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Canadian Institute for Advanced Research","funders":"Bayerisches Staatsministerium für Umwelt und Verbraucherschutz; Solar Technologies go Hybrid; Deutsche Forschungsgemeinschaft; Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS); U.S. Department of Energy","keywords":"Solubility; Bayesian probability; Computer science; Miscibility; Toolbox; Probabilistic logic; Flexibility (engineering); Set (abstract data type); Consistency (knowledge bases); Biological system; Algorithm; Chemistry; Artificial intelligence; Machine learning; Polymer; Mathematics; 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":[0.004162596,0.001090846,0.001511827,0.002061473,0.0008560181,0.001800347,0.00251371,0.002022635,0.003615139],"category_scores_gemma":[0.0126168,0.001113184,0.001520325,0.001503203,0.001599808,0.002862684,0.001654928,0.002763853,0.001030296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001382776,"about_ca_system_score_gemma":0.002196039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007895267,"about_ca_topic_score_gemma":0.00614783,"domain_scores_codex":[0.9983191,0.0006934671,0.00008218489,0.0003315543,0.0004473928,0.0001262282],"domain_scores_gemma":[0.9951427,0.003425079,0.000370655,0.0003447607,0.0005809735,0.0001359298],"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":[0.00007367611,0.00005719687,0.001697277,0.00008559506,0.00007741422,0.00008074255,0.00006860404,0.8982641,0.001341834,0.06466662,0.001861173,0.03172571],"study_design_scores_gemma":[0.000007948154,0.00001205671,0.0001665371,0.00001126226,0.00001011572,0.00001707361,0.000004652243,0.9694561,0.0003566805,0.02910388,0.000840634,0.00001305225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0125532,0.0003588772,0.983916,0.0004082208,0.00003486493,0.00005087168,0.000424575,0.0003528932,0.001900529],"genre_scores_gemma":[0.6030546,0.00165406,0.3839881,0.0006989413,0.0003973311,0.0006071352,0.002499357,0.0004103901,0.006690013],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007895267,"threshold_uncertainty_score":0.02201414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04927837693124544,"score_gpt":0.3072492165675478,"score_spread":0.2579708396363024,"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."}}