{"id":"W2783478958","doi":"10.1039/c7nr08672e","title":"NANoPoLC algorithm for correcting nanoparticle concentration by sample polydispersity","year":2018,"lang":"en","type":"article","venue":"Nanoscale","topic":"Nanoparticle-Based Drug Delivery","field":"Materials Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; University of Ottawa Heart Institute Foundation","keywords":"Dispersity; Nanoparticle; Algorithm; Materials science; Sample (material); Computer science; Nanotechnology; Chemistry; Chromatography; Polymer chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002504793,0.001477375,0.0009286653,0.0016971,0.001127079,0.001396333,0.002105163,0.001598988,0.007393818],"category_scores_gemma":[0.0096294,0.0006950725,0.0006808875,0.001099889,0.0006176205,0.001205038,0.001350471,0.002326414,0.002321428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001214473,"about_ca_system_score_gemma":0.003016655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00545373,"about_ca_topic_score_gemma":0.008703297,"domain_scores_codex":[0.9989123,0.0001952322,0.0001275151,0.000275195,0.0004260365,0.00006365035],"domain_scores_gemma":[0.9970162,0.001510796,0.0002145494,0.0003429896,0.0008537943,0.00006164273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008320952,0.0002094853,0.003764583,0.0006563115,0.0003458917,0.0003260033,0.0004089633,0.1250081,0.0636182,0.02596644,0.03348296,0.7453809],"study_design_scores_gemma":[0.00007033624,0.00004326081,0.0003998415,0.00002195833,0.00002901154,0.0001219786,0.00001988054,0.9159293,0.05848823,0.003344406,0.02147918,0.00005272515],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003205918,0.0001875079,0.9827949,0.00008396066,0.00008243648,0.0000932644,0.0001866721,0.0127994,0.0005658873],"genre_scores_gemma":[0.02635936,0.0001157199,0.9690694,0.0001240408,0.00002280425,0.0003963584,0.0004818293,0.001830686,0.001599764],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007393818,"threshold_uncertainty_score":0.02473474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01331125100892876,"score_gpt":0.2508637039316862,"score_spread":0.2375524529227574,"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."}}