{"id":"W3033828193","doi":"10.1016/j.patter.2020.100040","title":"BIAFLOWS: A Collaborative Framework to Reproducibly Deploy and Benchmark Bioimage Analysis Workflows","year":2020,"lang":"en","type":"article","venue":"Patterns","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Regional Development Fund; Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii; Université de Liège; Academy of Finland; Agence Nationale de la Recherche; Ministerstvo Školství, Mládeže a Tělovýchovy; European Cooperation in Science and Technology; Francis Crick Institute; Innovation for Defence Excellence and Security","keywords":"Workflow; Benchmark (surveying); Computer science; Segmentation; Software; Data science; Data mining; Artificial intelligence; Database; Cartography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001834909,0.0002022317,0.0003004249,0.0001337684,0.00006217091,0.00008851358,0.0002267271,0.0001243531,0.0001059728],"category_scores_gemma":[0.0004429313,0.0002001156,0.0001447848,0.001235054,0.00003917427,0.000006328512,0.0002780086,0.0001195454,0.00002395523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000970759,"about_ca_system_score_gemma":0.0000245224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004844935,"about_ca_topic_score_gemma":0.0001241773,"domain_scores_codex":[0.9983592,0.00008759872,0.0002436111,0.0009271589,0.0001496729,0.000232753],"domain_scores_gemma":[0.9988266,0.00002159207,0.00008032211,0.0007089945,0.0001374387,0.0002250403],"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":[0.0001363061,0.00009056258,0.4255445,0.00004498719,0.001572104,0.00004397082,0.001395194,0.0001164807,0.5482531,0.00001518023,0.01654949,0.006238214],"study_design_scores_gemma":[0.0003826108,0.0007241789,0.0556496,0.00004803834,0.00151032,0.000004424262,0.0006092269,0.0008207473,0.891,0.0001395005,0.0480509,0.001060425],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8859946,0.0005782605,0.1110917,0.001418029,0.00001431539,0.0002939523,0.00003950857,0.00009175758,0.0004778944],"genre_scores_gemma":[0.9779541,0.0002843043,0.01773799,0.003533313,0.0002449788,0.00005415387,0.00008736951,0.00003626762,0.00006746943],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3698949,"threshold_uncertainty_score":0.8160475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00971103797528534,"score_gpt":0.271195268797925,"score_spread":0.2614842308226397,"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."}}