{"id":"W4402875125","doi":"10.1038/s42256-024-00903-w","title":"Development of AI-assisted microscopy frameworks through realistic simulation with pySTED","year":2024,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Mila - Quebec Artificial Intelligence Institute; Ontario Brain Institute","funders":"Fonds de recherche du Québec – Nature et technologies; Fonds de Recherche du Québec - Santé; Canada Research Chairs; National Science Foundation","keywords":"Microscopy; Nanotechnology; Computer science; Materials science; Physics; Optics","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":[0.0001575478,0.0001918023,0.0001812389,0.00007838288,0.00004709383,0.00004601661,0.0002294901,0.0005365855,0.00003365476],"category_scores_gemma":[0.0001204916,0.0001499394,0.00007627976,0.0003827869,0.00007930223,0.000008262952,0.00008129507,0.0007287274,0.000005168954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003060274,"about_ca_system_score_gemma":0.0001177103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003399575,"about_ca_topic_score_gemma":0.0001386011,"domain_scores_codex":[0.9988614,0.00003410315,0.000329758,0.0004165921,0.0001997029,0.000158495],"domain_scores_gemma":[0.9992472,0.00004390804,0.00008064619,0.0003866789,0.0002077969,0.00003380526],"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":[0.0002610927,0.0001590761,0.0006056527,0.0003248628,0.0004336808,0.00003653027,0.0005426464,0.005100127,0.92473,0.001078794,0.001651871,0.06507567],"study_design_scores_gemma":[0.00003638047,0.00009367964,0.0001600373,0.0001983698,0.00005970495,0.00001244698,0.00002297066,0.01030127,0.9593899,0.0003635677,0.02915636,0.0002052953],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0329671,0.004306351,0.9613754,0.00009800537,0.00005379956,0.00018268,0.000008852199,0.00008108157,0.000926733],"genre_scores_gemma":[0.9352383,0.00007163108,0.06378676,0.0003187137,0.00006063176,0.00001230746,0.0002772199,0.00003005141,0.0002043659],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9022712,"threshold_uncertainty_score":0.6114351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007806803370787348,"score_gpt":0.344372366455415,"score_spread":0.3365655630846276,"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."}}