{"id":"W2797749376","doi":"10.1016/j.cell.2018.03.040","title":"In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images","year":2018,"lang":"en","type":"article","venue":"Cell","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":680,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Montreal Clinical Research Institute","funders":"National Center for Research Resources; National Institute of General Medical Sciences; National Institute on Aging; National Institute of Neurological Disorders and Stroke; Google","keywords":"Biology; In silico; Fluorescence; Fluorescent labelling; Computational biology; Artificial intelligence; Genetics; Computer science; Gene","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":[0.0002906851,0.0001328785,0.000150401,0.0001120478,0.00002889577,0.00002172616,0.0002068452,0.0001258853,0.0000511361],"category_scores_gemma":[0.00009556861,0.0001372546,0.00004808695,0.0002282704,0.00008101152,0.000006243318,0.0001534263,0.0001183985,0.00003165951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002716678,"about_ca_system_score_gemma":0.00003568864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00015095,"about_ca_topic_score_gemma":0.0003516037,"domain_scores_codex":[0.9989384,0.00006121452,0.0002582753,0.000369586,0.00009236871,0.0002801198],"domain_scores_gemma":[0.9994316,0.00001085895,0.00006235672,0.0003737413,0.00007558622,0.00004583697],"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":[0.00001887794,0.0001174451,0.01970512,0.00001322138,0.0000060668,0.00001237997,0.00006648402,0.00000376343,0.9746131,0.000001656124,0.005000284,0.00044164],"study_design_scores_gemma":[0.0004758458,0.0001157763,0.001345002,0.00001852274,0.000008501578,0.000001611569,0.00004144744,0.0002065087,0.9888963,0.00005289621,0.008693828,0.0001438154],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913735,0.0006559099,0.0001406789,0.000083079,0.00003163904,0.0001521913,0.000001977333,0.00002448055,0.007536557],"genre_scores_gemma":[0.9963005,0.0002218901,0.001526438,0.0003151716,0.0001926285,0.00001720683,0.00003124262,0.0000224166,0.001372506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01836011,"threshold_uncertainty_score":0.559708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005955584155744669,"score_gpt":0.2585947286846439,"score_spread":0.2526391445288993,"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."}}