{"id":"W3164277722","doi":"10.1101/2021.05.24.445502","title":"Estimating the Dynamic Range of Quantitative Single-Molecule Localization Microscopy","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Ontario Ministry of Research, Innovation and Science","keywords":"Photobleaching; Range (aeronautics); Computer science; Dynamic range; Microscopy; Biological system; Duty cycle; Optics; Artificial intelligence; Materials science; Physics; Computer vision; Fluorescence; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003626563,0.0004568384,0.0004394602,0.00009354484,0.0001525613,0.0001163039,0.0006231539,0.0005021525,0.000007391067],"category_scores_gemma":[0.0004940802,0.0004387823,0.0001756426,0.0003235277,0.000372389,0.00001332642,0.0006849896,0.0004092744,0.000002639431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001129203,"about_ca_system_score_gemma":0.0003881899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002737913,"about_ca_topic_score_gemma":0.000007595554,"domain_scores_codex":[0.997744,0.0001986446,0.0005546971,0.0008795715,0.0002399151,0.0003831342],"domain_scores_gemma":[0.9972274,0.00003056071,0.0006200002,0.00130798,0.0007332385,0.00008087685],"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":[0.00003634919,0.000105046,0.001178777,0.0002661103,0.00008747144,0.000007961395,0.00001836135,0.001065796,0.9971199,0.00003811612,0.00007410717,0.000002004118],"study_design_scores_gemma":[0.0001973496,0.0001323385,0.001181591,0.0005342964,0.00006788332,3.58642e-8,0.00001229744,0.004302771,0.9929606,0.000002163881,0.0001678946,0.0004407862],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.477354,0.002678803,0.5189721,0.00004085089,0.0003184413,0.0004607518,0.0001015508,0.00007089072,0.000002624014],"genre_scores_gemma":[0.7485223,0.0001681055,0.250896,0.0001297332,0.0000749718,0.00009794271,0.000004398439,0.0001043808,0.000002185075],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2711683,"threshold_uncertainty_score":0.9998064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01090233456606254,"score_gpt":0.2714729305580257,"score_spread":0.2605705959919632,"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."}}