{"id":"W4394142287","doi":"10.6084/m9.figshare.21516278","title":"Optimization of HPLC method using central composite design for estimation of Torsemide and Eplerenone in tablet dosage form","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Analytical Methods in Pharmaceuticals","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Eplerenone; Chromatography; Composite number; High-performance liquid chromatography; Central composite design; Chemistry; Materials science; Medicine; Response surface methodology; Composite material; Internal medicine; Heart failure; Spironolactone","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003503345,0.0002351516,0.0006050533,0.0001330206,0.00004807246,0.00001937249,0.0002729251,0.0002739429,0.264792],"category_scores_gemma":[0.002984813,0.0002595657,0.00008977435,0.0002091529,0.00001706775,0.0001039788,0.0002411546,0.0003284385,8.202908e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001741631,"about_ca_system_score_gemma":0.0001036262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003475899,"about_ca_topic_score_gemma":0.000001006637,"domain_scores_codex":[0.9983314,0.0001533889,0.000646026,0.0003271522,0.0002612316,0.0002808583],"domain_scores_gemma":[0.9969518,0.002020704,0.0005496012,0.0003040727,0.00008854467,0.00008529964],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001358397,0.00009310203,0.000001554146,0.00589315,0.00007570294,0.000004795813,0.00002351363,0.4223062,0.0009264606,0.000003754303,0.5692838,0.001252168],"study_design_scores_gemma":[0.0006887484,0.00003359883,0.000002026657,0.001974355,0.0002521033,0.000007911435,0.00001405927,0.921625,0.02639084,0.0001685502,0.04856477,0.0002780202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000003234811,0.0001741232,0.09688783,0.000004418304,0.00002211084,0.0003533819,0.9025203,0.00001043005,0.00002419168],"genre_scores_gemma":[0.000007032095,0.00001300693,0.3494016,0.00001349172,0.00002360222,0.00008428074,0.6504292,0.00001972859,0.000007973341],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.520719,"threshold_uncertainty_score":0.9999856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1461562031103981,"score_gpt":0.421605306043929,"score_spread":0.275449102933531,"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."}}