{"id":"W2076448238","doi":"10.1093/bioinformatics/btu759","title":"Local statistics allow quantification of cell-to-cell variability from high-throughput microscope images","year":2014,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Cell; Protein subcellular localization prediction; Microscope; Subcellular localization; Microscopy; Biology; Computational biology; Cell biology; Cytoplasm; Pathology; Genetics; Gene; Medicine","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":[0.0004558362,0.0001867239,0.0002596985,0.00005505943,0.00004989466,0.00004176259,0.0003285929,0.0002121278,0.00003719258],"category_scores_gemma":[0.0001844778,0.0001823957,0.00007813795,0.0001208453,0.000139588,0.00001255247,0.0001755022,0.00010197,0.00007189905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002255518,"about_ca_system_score_gemma":0.00006120232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001975084,"about_ca_topic_score_gemma":0.00002141806,"domain_scores_codex":[0.9986715,0.0000775611,0.0006092274,0.0002477849,0.0001888166,0.000205135],"domain_scores_gemma":[0.9984004,0.0000668046,0.0002952314,0.0008993709,0.0002478551,0.0000902867],"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":[0.00002664222,0.000139828,0.0003684173,0.0001156943,0.00002476897,1.937594e-7,0.0001002763,0.0001451868,0.9739932,0.00007413069,0.0217222,0.003289486],"study_design_scores_gemma":[0.0002506712,0.0001807281,0.0008073305,0.000009816402,0.00006261945,3.763699e-7,0.00006697317,0.006090465,0.9808229,0.0001876764,0.01131216,0.0002083042],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1072726,0.00002653026,0.8905114,0.0000323004,0.00004061326,0.0001989209,0.0002112098,0.00002497254,0.001681431],"genre_scores_gemma":[0.6322484,0.00007251077,0.3663245,0.0001910489,0.00003507945,0.000007602514,0.0009242755,0.00001510734,0.0001814229],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5249758,"threshold_uncertainty_score":0.743788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005301778245642983,"score_gpt":0.2398288840949953,"score_spread":0.2345271058493524,"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."}}