{"id":"W4365514466","doi":"10.1017/s2633903x23000090","title":"Imaging and Molecular Annotation of Xenographs and Tumours (IMAXT): High throughput data and analysis infrastructure","year":2023,"lang":"en","type":"article","venue":"Biological Imaging","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Wellcome Trust; Ovarian Cancer Research Alliance; National Cancer Institute; Cancer Research UK; UK Research and Innovation; Memorial Sloan-Kettering Cancer Center","keywords":"Annotation; Throughput; Computer science; Computational biology; Artificial intelligence; Biology; Operating system; Wireless","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":[0.002878204,0.001414021,0.001207532,0.00271559,0.001140134,0.003351409,0.002902417,0.001241203,0.01409721],"category_scores_gemma":[0.004575684,0.001135321,0.001372962,0.003146516,0.0008719462,0.002981138,0.004219459,0.001816692,0.01171756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001588309,"about_ca_system_score_gemma":0.002756363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00337138,"about_ca_topic_score_gemma":0.003674828,"domain_scores_codex":[0.997787,0.0002296296,0.0001652336,0.0006022681,0.001010464,0.000205466],"domain_scores_gemma":[0.9966341,0.0004544737,0.0002890521,0.001369755,0.0008799529,0.0003726007],"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":[0.00286865,0.0005739013,0.01604782,0.001603843,0.0006159576,0.001747269,0.001436752,0.03872213,0.3079005,0.03075158,0.2757173,0.3220144],"study_design_scores_gemma":[0.0002516027,0.0004675988,0.02571706,0.0002377807,0.0002132762,0.002636538,0.0004795795,0.2540315,0.3517023,0.02812538,0.3356685,0.0004688933],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01945252,0.0006450745,0.799709,0.0007519927,0.0001697023,0.0005428473,0.03855988,0.1295688,0.01060005],"genre_scores_gemma":[0.1475251,0.0009662623,0.6960667,0.0004647041,0.0001478355,0.001540476,0.1301361,0.01594629,0.007206437],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01409721,"threshold_uncertainty_score":0.04715985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01053911184589587,"score_gpt":0.2818309682109392,"score_spread":0.2712918563650433,"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."}}