{"id":"W6912976679","doi":"10.5683/sp3/nvkvwe","title":"Data and code for \"Machine learning modelling in predicting and optimizing PLGA nanoparticle encapsulation efficiency and therapeutic efficacy\"","year":2023,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Encapsulation (networking); Gaussian process; Bayesian probability; PLGA; Bayesian optimization; Data modeling; Bayesian inference; Experimental data","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":[0.001716558,0.003680923,0.001527914,0.00206255,0.001127048,0.001778817,0.004182689,0.00351469,0.03249131],"category_scores_gemma":[0.005238213,0.0007001074,0.00206964,0.002733776,0.0008225278,0.0009922695,0.001852963,0.003091787,0.05247853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00248572,"about_ca_system_score_gemma":0.002943533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03776392,"about_ca_topic_score_gemma":0.08241263,"domain_scores_codex":[0.998364,0.0002458249,0.0001441356,0.0003846835,0.0006330438,0.0002283543],"domain_scores_gemma":[0.9974734,0.0006360787,0.000173909,0.000663833,0.0008115968,0.0002410983],"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":[0.000116209,0.0001394742,0.0006621685,0.0003673259,0.00003156326,0.00002904219,0.00001297442,0.001407258,0.0002807607,0.0004226726,0.9930124,0.003518159],"study_design_scores_gemma":[0.0007521218,0.0001394538,0.005061158,0.0003062024,0.0000711332,0.000181731,0.0001270412,0.007522582,0.002951144,0.003927015,0.978861,0.00009935202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000938634,0.0001716728,0.0004799167,0.0002708745,0.0001344739,0.00007593591,0.9945093,0.001494415,0.001924828],"genre_scores_gemma":[0.001102448,0.0000590133,0.001224069,0.0001249379,0.0000100459,0.000181341,0.9959024,0.0001214729,0.001274351],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03776392,"threshold_uncertainty_score":0.1086942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08306000702713691,"score_gpt":0.3209912612167258,"score_spread":0.2379312541895889,"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."}}