{"id":"W2224015147","doi":"10.1111/jphp.12508","title":"Support vector regression to estimate the permeability enhancement of potential transdermal enhancers","year":2016,"lang":"en","type":"article","venue":"Journal of Pharmacy and Pharmacology","topic":"Advancements in Transdermal Drug Delivery","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Support vector machine; Transdermal; Regression; Regression analysis; Linear regression; Computer science; Nonlinear regression; Kernel (algebra); Machine learning; Mathematics; Statistics; Pharmacology; Medicine","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":[0.001762425,0.0006736735,0.0006727406,0.0004660086,0.00008626727,0.0005437342,0.0004612344,0.0007121246,0.000661525],"category_scores_gemma":[0.00410743,0.0002857361,0.000726453,0.0004043911,0.0002668453,0.0006359966,0.000323164,0.001116377,0.0002227631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003460216,"about_ca_system_score_gemma":0.0004359669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001522886,"about_ca_topic_score_gemma":0.0009595855,"domain_scores_codex":[0.9994594,0.0002445328,0.0000317264,0.00008233584,0.0001519881,0.00003001862],"domain_scores_gemma":[0.9977449,0.00166661,0.0002423018,0.00005511678,0.0002610057,0.00003019514],"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":[0.0002028414,0.00009897391,0.002172007,0.0002213173,0.00009659096,0.00005893252,0.00003299638,0.9040223,0.02597277,0.001845709,0.0003530298,0.06492251],"study_design_scores_gemma":[0.000002880846,0.0000651709,0.0002052432,0.000003544894,0.000008128974,0.000008564848,0.000003324084,0.9960394,0.003290965,0.0002362039,0.0001324764,0.00000417583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1140998,0.0007260588,0.883888,0.0001561221,0.00002617276,0.00004185999,0.00009022876,0.0003937535,0.0005779679],"genre_scores_gemma":[0.8924253,0.0005125304,0.1051473,0.00004735432,0.00001946977,0.00006934522,0.000150344,0.00004684487,0.001581483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001762425,"threshold_uncertainty_score":0.009320676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05647977061569085,"score_gpt":0.4628880707865853,"score_spread":0.4064083001708945,"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."}}