{"id":"W3203964529","doi":"10.1115/1.4052578","title":"Using Parallel Coordinates in Optimization of Nano-Particle Drug Delivery","year":2021,"lang":"en","type":"article","venue":"Journal of Biomechanical Engineering","topic":"Nanoparticle-Based Drug Delivery","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Biodistribution; Computer science; Drug delivery; Optimal design; Personalization; Nanoparticle; Distribution (mathematics); Mathematical optimization; Nanotechnology; Materials science; Mathematics; Machine learning; 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":[0.000670052,0.0001050234,0.0003064745,0.0001423196,0.00001965784,0.00003125018,0.0001572643,0.00005479779,0.0001018503],"category_scores_gemma":[0.0002369234,0.0001018122,0.00009090576,0.0005231908,0.00001971271,0.000346283,0.00007544355,0.0001104304,0.000004112242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001145568,"about_ca_system_score_gemma":0.0001207255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001994409,"about_ca_topic_score_gemma":0.000002914519,"domain_scores_codex":[0.9985778,0.0000620452,0.0006867538,0.0001298535,0.000283917,0.0002596533],"domain_scores_gemma":[0.9991549,0.0001356833,0.0002245259,0.0001334426,0.0002394981,0.0001119331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002852928,0.00007125929,0.00004947119,0.00002156888,0.000005573647,0.00005632169,0.00003037901,0.4368828,0.5626935,0.00009302355,0.000004072015,0.0000635214],"study_design_scores_gemma":[0.0004873115,0.00003360676,0.00001960862,0.0001142121,0.00001464087,0.00004883124,0.00004879873,0.3565032,0.642618,0.00003543827,0.000006679699,0.00006960258],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9071934,0.0005973856,0.09171194,0.00009638551,0.00034267,0.00003930772,0.000003158994,0.0000134575,0.000002266399],"genre_scores_gemma":[0.9275513,0.00003572322,0.07232421,0.00002002048,0.00004831948,7.51377e-7,5.063411e-7,0.00001476349,0.000004465741],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08037953,"threshold_uncertainty_score":0.4151782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0157489462932427,"score_gpt":0.2339274726709754,"score_spread":0.2181785263777327,"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."}}