{"id":"W4318586840","doi":"10.2139/ssrn.4332890","title":"Artificial Tumor Matrices for Tumoroid Generation","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Molecular Communication and Nanonetworks","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence","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":[0.0001164011,0.0002651074,0.0001158843,0.0002362563,0.0001358374,0.0004582729,0.0001849545,0.0003814324,0.002549367],"category_scores_gemma":[0.000294414,0.0001126176,0.0001149218,0.0001856694,0.0001923867,0.0003374228,0.0003214108,0.0003773317,0.0007265132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002677648,"about_ca_system_score_gemma":0.0001369375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000247881,"about_ca_topic_score_gemma":0.0004548201,"domain_scores_codex":[0.9999301,0.00001435182,0.00000219523,0.00001601517,0.00002827337,0.000009057012],"domain_scores_gemma":[0.9998808,0.00004423254,0.00001968108,0.00001512366,0.00002225909,0.00001789365],"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":[0.0001145401,0.00005036643,0.0001063425,0.0001505743,0.00001079513,0.0001420044,0.00009453762,0.01808035,0.9259036,0.03534215,0.001515872,0.01848873],"study_design_scores_gemma":[0.00003279779,0.0001660138,0.0001820872,0.00001460474,0.00001216546,0.0002252437,0.00004417021,0.1178594,0.8492382,0.007574422,0.02463293,0.00001805158],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5372148,0.004418258,0.3977998,0.000913934,0.0005106952,0.0001688872,0.0006634469,0.0009641295,0.0573461],"genre_scores_gemma":[0.9452834,0.0009724389,0.04070545,0.00008324388,0.00001901226,0.00006548697,0.0001720495,0.00009340805,0.01260559],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002549367,"threshold_uncertainty_score":0.008528531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0304494494783048,"score_gpt":0.258577097299782,"score_spread":0.2281276478214772,"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."}}