{"id":"W2613401964","doi":"10.1016/j.bpj.2017.03.020","title":"Molecular Counting with Localization Microscopy: A Bayesian Estimate Based on Fluorophore Statistics","year":2017,"lang":"en","type":"article","venue":"Biophysical Journal","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Arkansas Biosciences Institute","keywords":"Fluorophore; Microscopy; Biological system; Fluorescence microscope; Coefficient of variation; Physics; Fluorescence; Mathematics; Statistics; Optics; Biology","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":[0.00009557853,0.0002157694,0.0001679881,0.00003705134,0.0005511555,0.0002615862,0.0003672368,0.0001067178,0.000007581242],"category_scores_gemma":[0.0001085283,0.0001777396,0.00005856352,0.00003836622,0.000275909,0.00001269894,0.00006485426,0.0002187768,0.00000620456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003743658,"about_ca_system_score_gemma":0.0001104087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006285039,"about_ca_topic_score_gemma":0.000002666542,"domain_scores_codex":[0.9989318,0.00003724223,0.0001839964,0.0003031451,0.000239674,0.0003041171],"domain_scores_gemma":[0.9988825,0.000007640994,0.0002801487,0.0005093278,0.0001875953,0.0001327261],"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":[0.0002156682,0.00008625662,0.001297669,0.00001368847,0.00001648414,0.00008527856,0.000005595716,0.001366063,0.9946741,0.0001372594,0.0005030357,0.001598913],"study_design_scores_gemma":[0.0005032946,0.0007725181,0.0006067706,0.0001191031,0.00002369742,0.0000349884,0.000005422726,0.02107392,0.9756575,0.0001302953,0.0008222748,0.0002502649],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1002884,0.00001307925,0.8992114,0.0001080634,0.00006952986,0.000121722,0.00004199853,0.00001995782,0.0001258331],"genre_scores_gemma":[0.8253391,0.00001652561,0.1740023,0.0003333208,0.0001863525,0.000007210735,0.00004967216,0.00004697658,0.00001852684],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7252091,"threshold_uncertainty_score":0.7248012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005740325109076684,"score_gpt":0.2932792975708768,"score_spread":0.2875389724618001,"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."}}