{"id":"W2750846844","doi":"","title":"Upconverting nanoparticles for integration in bioimaging and therapeutic applications.","year":2017,"lang":"en","type":"article","venue":"EspaceINRS Institutional Digital Repository (Institut National de la Recherche Scientifique)","topic":"Nanoparticle-Based Drug Delivery","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Fonds de recherche du Québec – Nature et technologies; Alexander von Humboldt-Stiftung","keywords":"Nanotechnology; Nanoparticle; Computer science; Materials science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003494005,0.0002010614,0.0001787094,0.0002149716,0.0012396,0.001872438,0.0004370825,0.000200205,0.000006054699],"category_scores_gemma":[0.004089574,0.0001981886,0.00007970488,0.000205916,0.001342658,0.001756005,0.000127419,0.0002685965,0.00002777168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009180252,"about_ca_system_score_gemma":0.001169025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000442265,"about_ca_topic_score_gemma":0.00003017691,"domain_scores_codex":[0.9978718,0.0002557807,0.0004219588,0.0005881886,0.0005104881,0.0003517861],"domain_scores_gemma":[0.9976758,0.001187677,0.0002451,0.0003177095,0.0004306601,0.0001431015],"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":[0.00007666424,0.0001595093,0.003930863,0.00004941413,0.000008441832,0.000008987713,0.0002073241,0.001159395,0.8712083,0.1082099,0.0000650835,0.01491609],"study_design_scores_gemma":[0.001188174,0.0000289883,0.01356984,0.0002884117,0.00002108794,0.0001105189,0.0001144753,0.01974845,0.9055621,0.05083465,0.008115115,0.0004181905],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9401493,0.0004101585,0.03902866,0.001074018,0.0004550072,0.0009375259,0.00006864872,0.0001248997,0.01775175],"genre_scores_gemma":[0.9857993,0.0000114551,0.01262108,0.0001358065,0.0001327927,0.0004071651,0.00001698324,0.0000151696,0.0008602279],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05737526,"threshold_uncertainty_score":0.9991637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08166081644263921,"score_gpt":0.3382789763053017,"score_spread":0.2566181598626625,"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."}}