{"id":"W4294391398","doi":"10.22159/ijpps.2022v14i9.44728","title":"PREPARATION, CHARACTERIZATION, AND OPTIMIZATION OF MEBENDAZOLE SPHERICAL AGGLOMERATES USING MODIFIED EVAPORATIVE PRECIPITATION IN AQUEOUS SOLUTION (EPAS)","year":2022,"lang":"en","type":"article","venue":"International Journal of Pharmacy and Pharmaceutical Sciences","topic":"Drug Solubulity and Delivery Systems","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network","funders":"All India Council for Technical Education","keywords":"Mebendazole; Agglomerate; Materials science; Dissolution; Aqueous solution; Solubility; Precipitation; Chromatography; Differential scanning calorimetry; Chemical engineering; Nuclear chemistry; Chemistry; Composite material; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002036098,0.0001469129,0.0002586693,0.0002817372,0.0003037813,0.00005471662,0.0002914948,0.00005507002,0.0005708612],"category_scores_gemma":[0.0001247521,0.0001409443,0.00005502013,0.0004091942,0.0004114065,0.0008185228,0.0001441319,0.0004361704,7.055574e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001063287,"about_ca_system_score_gemma":0.0002025446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005859374,"about_ca_topic_score_gemma":0.00000476776,"domain_scores_codex":[0.9972906,0.0008668159,0.0008203,0.000234959,0.0005802997,0.0002070032],"domain_scores_gemma":[0.9984632,0.0003308851,0.0006342915,0.00003793866,0.0003866965,0.0001469606],"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":[0.003390072,0.000801902,0.02746289,0.00006732649,0.0002238574,0.00003998879,0.005528609,0.7466971,0.1773974,0.001611275,0.0001384531,0.03664114],"study_design_scores_gemma":[0.0025998,0.000239611,0.0009276683,0.00002716917,0.00008211216,0.0002710813,0.0005679316,0.9733018,0.01397789,0.0003399646,0.007495556,0.0001694261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9772341,0.001873841,0.01704105,0.001361401,0.001851243,0.0002973531,0.00008577253,0.00001157351,0.0002436404],"genre_scores_gemma":[0.9971664,0.0009689568,0.001101707,0.0004681904,0.0002222,0.00001172759,0.00002268433,0.000006950595,0.00003116239],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2266047,"threshold_uncertainty_score":0.625053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1531258025540232,"score_gpt":0.4763409432970208,"score_spread":0.3232151407429976,"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."}}