{"id":"W3194988201","doi":"10.1093/bioinformatics/btab589","title":"PyJAMAS: open-source, multimodal segmentation and analysis of microscopy images","year":2021,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ted Rogers Centre for Heart Research; Hospital for Sick Children; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Canadian Institutes of Health Research; Government of Ontario","keywords":"Python (programming language); Scripting language; Computer science; Graphical user interface; Open source; Segmentation; Software; Extensibility; Mac OS; Image segmentation; Open source software; Interface (matter); Image processing; Computer graphics (images); User interface; Artificial intelligence; Data mining; Programming language; Operating system; Image (mathematics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000145772,0.00009676687,0.0002121966,0.0001083673,0.00004091818,0.00008119929,0.0001492645,0.00007215863,0.00003803444],"category_scores_gemma":[0.00006207801,0.00009465271,0.00009595147,0.0003395661,0.00006534219,0.0000167421,0.0003060445,0.00003389833,0.000001867024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000736728,"about_ca_system_score_gemma":0.0000383689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003092206,"about_ca_topic_score_gemma":0.00002968667,"domain_scores_codex":[0.9993321,0.00002502348,0.00031493,0.0001344467,0.00009023174,0.0001032122],"domain_scores_gemma":[0.9992983,0.00001084109,0.0001654454,0.0003318932,0.0001535371,0.0000400354],"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":[0.00001485994,0.00005611301,0.008851926,0.00004354011,0.000676734,0.000001081616,0.0001985391,0.00005320281,0.9768285,0.000006365441,0.003660351,0.009608782],"study_design_scores_gemma":[0.000265875,0.00004696224,0.002836544,0.0000071601,0.0004891334,0.00000353756,0.0004587652,0.006395866,0.9847342,0.000004996752,0.00463994,0.0001169735],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8473498,0.0006498845,0.1465238,0.00004696233,0.00001447428,0.0002185816,0.00006515288,0.00002156635,0.005109762],"genre_scores_gemma":[0.6112265,0.001382728,0.3824618,0.0005607066,0.0000295116,0.00001839478,0.00215613,0.00002254189,0.002141719],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2361233,"threshold_uncertainty_score":0.3859825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00716577433905271,"score_gpt":0.2882043148524268,"score_spread":0.2810385405133741,"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."}}