{"id":"W2939354810","doi":"10.7554/elife.45239","title":"Opto-magnetic capture of individual cells based on visual phenotypes","year":2019,"lang":"en","type":"article","venue":"eLife","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Hôpital Maisonneuve-Rosemont","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Genome Canada; Canadian Institutes of Health Research; Canadian Cancer Society; Fonds de Recherche du Québec - Santé","keywords":"Population; Confocal; Cell sorting; Biology; Phenotype; Cell biology; Isolation (microbiology); Nanotechnology; Biophysics; Cell; Materials science; Physics; Bioinformatics; Gene; Genetics; Optics","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":[0.0002171687,0.0002493999,0.0002880775,0.0002404138,0.0002644748,0.0005145317,0.0003459832,0.0003819648,0.0007021613],"category_scores_gemma":[0.0002509613,0.0001611154,0.0001737919,0.0001357404,0.0004229179,0.000273454,0.0004912953,0.0004743327,0.0004992238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003205509,"about_ca_system_score_gemma":0.0002524598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005914921,"about_ca_topic_score_gemma":0.001964917,"domain_scores_codex":[0.9998329,0.00001408549,0.000009794993,0.00005230838,0.00005713453,0.00003388546],"domain_scores_gemma":[0.9998456,0.00004754896,0.00003506131,0.00002413505,0.00002547354,0.0000220616],"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":[0.00001906457,0.000005713059,0.0001428799,0.00001620715,0.000001846432,0.00001786423,0.00001403015,0.00006482389,0.9977298,0.0002423671,0.00003335388,0.001712023],"study_design_scores_gemma":[0.000007664661,0.00005177405,0.001585196,0.000005010612,0.000006315515,0.0001347118,0.00003190693,0.002162735,0.9932144,0.0001672984,0.002626376,0.000006640212],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8170519,0.000912748,0.1748407,0.0003651039,0.0001010186,0.0001857937,0.000590176,0.000360261,0.005592225],"genre_scores_gemma":[0.9113206,0.0008744376,0.0800507,0.0002491988,0.00002762463,0.0002402235,0.0004569998,0.0001054247,0.006674705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007021613,"threshold_uncertainty_score":0.002349019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006167996849397467,"score_gpt":0.216341107462654,"score_spread":0.2101731106132565,"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."}}