{"id":"W4213155806","doi":"10.32920/19189481","title":"Ryerson researchers develop first-of-its-kind lensless platform for tissue imaging","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Extracellular vesicles in disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Erlangen Graduate School of Advanced Optical Technologies; Deutsche Forschungsgemeinschaft; National Science Foundation","keywords":"Microcirculation; Cancer research; Medicine; Pathology; Nanotechnology; Biomedical engineering; Internal medicine; Materials science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001940215,0.001920006,0.0008352435,0.00222717,0.0009891358,0.002409084,0.001971478,0.002275507,0.05439402],"category_scores_gemma":[0.001522789,0.0006963813,0.00122636,0.0008797743,0.000691504,0.003275592,0.002203046,0.002738371,0.05347861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001079321,"about_ca_system_score_gemma":0.0009314367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001618981,"about_ca_topic_score_gemma":0.001538911,"domain_scores_codex":[0.9989609,0.00007227719,0.00003089652,0.000225428,0.0005973694,0.0001132502],"domain_scores_gemma":[0.9984654,0.0001497237,0.00006810351,0.0004672882,0.00057386,0.0002756809],"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":[0.000310205,0.0002010778,0.0009779865,0.0005237631,0.0001099696,0.0004220941,0.0003489275,0.001547724,0.2161264,0.09635109,0.2191708,0.4639099],"study_design_scores_gemma":[0.00007194425,0.0002043219,0.0007262772,0.00005938036,0.00004123397,0.0006181265,0.00007228268,0.009676506,0.2003929,0.01302426,0.7750143,0.00009854557],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01583664,0.007697003,0.7070528,0.007523723,0.005516545,0.0007127245,0.00407807,0.04879051,0.202792],"genre_scores_gemma":[0.06865176,0.007741383,0.4445086,0.00175504,0.0009605765,0.0004825545,0.005683195,0.00781727,0.4623998],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05439402,"threshold_uncertainty_score":0.1819661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05705900831962368,"score_gpt":0.3475493153013723,"score_spread":0.2904903069817487,"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."}}