{"id":"W3004731352","doi":"10.1016/j.bpj.2019.11.1747","title":"Counting Proteins and Nucleic Acids with Single-molecule Microscopy","year":2020,"lang":"en","type":"article","venue":"Biophysical Journal","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Nucleic acid; Microscopy; Computational biology; Biological system; DNA; Nanotechnology; Chemistry; Computer science; Biophysics; Biology; Physics; Materials science; Optics; Biochemistry","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":[0.00004162624,0.0001617188,0.0001433771,0.00001567948,0.0001319666,0.00009515717,0.0001574137,0.00007898672,0.000005383712],"category_scores_gemma":[0.0000374259,0.0001303286,0.00004683069,0.00008759743,0.0001884441,0.00001179077,0.00009854064,0.000221006,0.000004806504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001358959,"about_ca_system_score_gemma":0.00004193818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001174013,"about_ca_topic_score_gemma":4.874622e-7,"domain_scores_codex":[0.9991795,0.0000267179,0.0001454271,0.0002741971,0.000128128,0.0002459864],"domain_scores_gemma":[0.999523,0.000003426127,0.00009945685,0.0001159936,0.00007866453,0.000179496],"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":[0.0001548092,0.00004896881,0.0005711642,0.00001237313,0.00001814338,0.00001949904,0.00004056613,0.000001598046,0.9981743,0.0000231351,0.0002524452,0.0006830518],"study_design_scores_gemma":[0.0003395821,0.001274521,0.0002093582,0.00003445924,0.00001216317,0.0001031917,0.00003576086,0.00004300864,0.9937611,0.00001682778,0.003986439,0.0001836057],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9098934,0.00009383639,0.08911876,0.0005801021,0.00002050721,0.0001499692,0.00000671528,0.00003120001,0.0001055287],"genre_scores_gemma":[0.9271345,0.00004321289,0.07146928,0.0008375875,0.0004510441,0.000005709255,0.000006490976,0.00003475987,0.00001743728],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01764949,"threshold_uncertainty_score":0.5314645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009022172269565172,"score_gpt":0.2523530371095511,"score_spread":0.2433308648399859,"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."}}