{"id":"W4287029212","doi":"10.5281/zenodo.5156049","title":"Automated assignment of cell identity from single-cell multiplexed imaging and proteomic data","year":2021,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute","funders":"","keywords":"Cell; Identity (music); Multiplexing; Computational biology; Computer science; Biology; Genetics; Physics; Telecommunications","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":[0.003777394,0.001464406,0.001428789,0.006030357,0.00178843,0.00362914,0.001250788,0.001196441,0.02971417],"category_scores_gemma":[0.007478874,0.0008840541,0.001205342,0.004417786,0.0005528142,0.001650238,0.002615889,0.001617437,0.02805453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001141181,"about_ca_system_score_gemma":0.001958588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002610874,"about_ca_topic_score_gemma":0.004429053,"domain_scores_codex":[0.9978487,0.0001628571,0.0002036531,0.0008307751,0.0006848965,0.0002690355],"domain_scores_gemma":[0.9958383,0.001235302,0.0003944651,0.001118262,0.001182416,0.0002313336],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002472015,0.0001648166,0.04154968,0.004660515,0.0004419214,0.0007095265,0.001024809,0.003096189,0.3138742,0.006337559,0.361972,0.2636968],"study_design_scores_gemma":[0.0002413743,0.0002583732,0.113279,0.0009012555,0.0005010476,0.001784575,0.001123648,0.04074487,0.3454887,0.01489954,0.4803464,0.0004313595],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.08224258,0.002720952,0.2127199,0.001007136,0.001191869,0.0006054252,0.6196034,0.06042046,0.01948826],"genre_scores_gemma":[0.1121128,0.001460735,0.3315603,0.0006123013,0.0002505065,0.001847333,0.5290002,0.01108635,0.01206957],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02971417,"threshold_uncertainty_score":0.0994038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02537385925882489,"score_gpt":0.2592895693987049,"score_spread":0.23391571013988,"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."}}