{"id":"W3092405354","doi":"10.1101/2020.10.07.328005","title":"Deep-LUMEN Assay – Human lung epithelial spheroid classification from brightfield images using deep learning","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Spheroid; Lumen (anatomy); Deep learning; Biology; Cell biology; Artificial intelligence; Computer science; Cell culture","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":[0.0009781129,0.0006503252,0.0004187793,0.0007261667,0.0002082162,0.0007845758,0.0005875973,0.001054821,0.001255977],"category_scores_gemma":[0.001082251,0.0002621296,0.0005269274,0.0003348782,0.0003324226,0.000485793,0.0006916245,0.0009385049,0.0006328105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006951136,"about_ca_system_score_gemma":0.0005677655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002559166,"about_ca_topic_score_gemma":0.002657505,"domain_scores_codex":[0.9994634,0.0001233339,0.0000315838,0.000152469,0.0001585576,0.00007063073],"domain_scores_gemma":[0.999486,0.0001679897,0.0000761893,0.00008222831,0.0001280363,0.00005954336],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007675888,0.0005943471,0.01106362,0.0005256925,0.0001666949,0.0004413404,0.0001436516,0.108472,0.6603719,0.002230526,0.01127301,0.2039496],"study_design_scores_gemma":[0.00003060029,0.000242106,0.004146829,0.00002364249,0.00002045763,0.0001941647,0.00004577555,0.7378721,0.2530388,0.001241367,0.003102715,0.00004141274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3877637,0.001613564,0.5918682,0.0007954842,0.0001748746,0.0002841772,0.003296153,0.01066653,0.003537263],"genre_scores_gemma":[0.7716162,0.0006592757,0.2192988,0.000335515,0.0000261375,0.0003054913,0.003915987,0.0002546793,0.00358804],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002559166,"threshold_uncertainty_score":0.005172789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02396633360794428,"score_gpt":0.2596684010759343,"score_spread":0.23570206746799,"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."}}