{"id":"W2931883352","doi":"10.1016/j.ejps.2019.03.024","title":"New unique PAT method and instrument for real-time inline size characterization of concentrated, flowing nanosuspensions","year":2019,"lang":"en","type":"article","venue":"European Journal of Pharmaceutical Sciences","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Association for Health Services and Policy Research","funders":"","keywords":"Dynamic light scattering; Sizing; Process analytical technology; Characterization (materials science); Particle size; Dispersion (optics); Process engineering; Light scattering; Process (computing); Process control; Computer science; Materials science; Scattering; Nanotechnology; Optics; Chemistry; Nanoparticle; Physics; Chemical engineering; Engineering","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":[0.0007125604,0.0009662359,0.0007918042,0.001075316,0.0004710374,0.0006523563,0.0009675348,0.001110285,0.001335271],"category_scores_gemma":[0.0008548892,0.0006658961,0.0004387828,0.0006300176,0.0005509381,0.001291619,0.0008183892,0.001624491,0.0009082694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003362657,"about_ca_system_score_gemma":0.0005489576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000154577,"about_ca_topic_score_gemma":0.0004295993,"domain_scores_codex":[0.9991581,0.00006826127,0.00005888149,0.0003018724,0.0003617201,0.00005113196],"domain_scores_gemma":[0.9992774,0.0002108438,0.0001353659,0.0001245812,0.0001815583,0.00007027991],"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":[0.00003193356,0.00003163034,0.000324295,0.00007258738,0.00001088102,0.00004245471,0.00002459841,0.00008249558,0.9873439,0.0003718812,0.000300825,0.01136262],"study_design_scores_gemma":[0.00001195719,0.00009382905,0.001245416,0.000005110076,0.00001991544,0.000559477,0.00001317229,0.005593927,0.9869533,0.0002140662,0.005261517,0.00002824687],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09851471,0.001612401,0.8913207,0.0004664795,0.0003836589,0.0003694273,0.0008821129,0.004425387,0.002025078],"genre_scores_gemma":[0.2168261,0.001023349,0.7746506,0.0006753217,0.0002038837,0.0008998935,0.0008955056,0.0003027715,0.004522725],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001335271,"threshold_uncertainty_score":0.004466891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02841066250412185,"score_gpt":0.2913475867804531,"score_spread":0.2629369242763313,"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."}}