{"id":"W4312180902","doi":"10.1083/jcb.202209127","title":"CLEM <i>Site</i> , a software for automated phenotypic screens using light microscopy and FIB-SEM","year":2022,"lang":"en","type":"article","venue":"The Journal of Cell Biology","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fibics (Canada)","funders":"Deutsche Forschungsgemeinschaft; European Molecular Biology Laboratory","keywords":"Microscopy; Software; Materials science; Computer science; Nanotechnology; Computer graphics (images); Optics; Physics; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000636255,0.0001268187,0.000227283,0.00007210433,0.0002309566,0.00001336269,0.0002930089,0.00008379156,0.00002369994],"category_scores_gemma":[0.00006243519,0.00009324693,0.0001183563,0.0001126115,0.00009021572,0.000003948301,0.0002553764,0.0001600336,5.760525e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002456842,"about_ca_system_score_gemma":0.00008619149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001750319,"about_ca_topic_score_gemma":0.000008091994,"domain_scores_codex":[0.9990401,0.0002257262,0.0003150929,0.0001487026,0.0000642895,0.0002060968],"domain_scores_gemma":[0.999127,0.0000563455,0.0003644019,0.0002327913,0.0001724704,0.00004701559],"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":[0.0002623928,0.00003883336,0.000374576,0.00001018704,0.00006917969,0.000002199801,0.00006439896,0.0001001822,0.9934363,0.00000252956,0.005185827,0.0004534065],"study_design_scores_gemma":[0.0005238179,0.0008844394,0.00005133474,0.000004877289,0.00016269,0.0001805006,0.00009802578,0.0006157295,0.937461,0.0001208899,0.05977256,0.0001241735],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9825444,0.003437948,0.01352514,0.0001536214,0.00005264334,0.0001590218,0.00002453455,0.00002175789,0.00008099322],"genre_scores_gemma":[0.9817215,0.0003931235,0.0167347,0.0007347736,0.0001620443,0.000005499167,0.00004836429,0.00002599867,0.0001739997],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05597532,"threshold_uncertainty_score":0.3802499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00801356675953956,"score_gpt":0.273365001548429,"score_spread":0.2653514347888895,"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."}}