{"id":"W2904241152","doi":"10.1208/s12249-018-1244-4","title":"Solubility Prediction of Drugs in Binary Solvent Mixtures at Various Temperatures Using a Minimum Number of Experimental Data Points","year":2018,"lang":"en","type":"article","venue":"AAPS PharmSciTech","topic":"Crystallization and Solubility Studies","field":"Materials Science","cited_by":61,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"CNIB","keywords":"Solubility; Outlier; Data point; Binary number; Solvent; Mathematics; Statistics; Chemistry; Thermodynamics; Computer science; Organic chemistry; Physics; Arithmetic","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007879197,0.0007281184,0.0006977282,0.0005639943,0.0003660217,0.0003954589,0.0004747004,0.0005622121,0.000897094],"category_scores_gemma":[0.002120897,0.000379689,0.0008656382,0.0004949472,0.0003004582,0.0008240062,0.0003135196,0.0007072823,0.0005398336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005421216,"about_ca_system_score_gemma":0.0007652992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002395155,"about_ca_topic_score_gemma":0.003070267,"domain_scores_codex":[0.9995586,0.00005470592,0.00005406811,0.000107346,0.0001891561,0.00003607395],"domain_scores_gemma":[0.9992655,0.0003121881,0.0001269005,0.0000598296,0.0002148277,0.00002079227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001898814,0.0003855783,0.006266278,0.0005455242,0.0000779584,0.0001241502,0.00007651415,0.06596123,0.899069,0.0006373562,0.0005818969,0.02437571],"study_design_scores_gemma":[0.00003679461,0.0004345702,0.00449801,0.00001528176,0.00004209569,0.00006940201,0.0000260031,0.1772747,0.8165781,0.0002494036,0.0007477818,0.00002775073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9130017,0.0008058336,0.08125599,0.0001574414,0.0000383127,0.0001676737,0.001875128,0.0005877645,0.002110266],"genre_scores_gemma":[0.9516813,0.0005201314,0.0444758,0.00002801229,0.00000693407,0.0002037799,0.002039071,0.00008328637,0.0009616832],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002395155,"threshold_uncertainty_score":0.004762411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05039244259021514,"score_gpt":0.3460640688888448,"score_spread":0.2956716262986296,"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."}}