{"id":"W2913036475","doi":"10.1016/j.bpj.2018.11.2367","title":"Optimizing Astigmatism for 3D Stochastic Optical Reconstruction Microscopy","year":2019,"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":"York University","funders":"","keywords":"Microscopy; Stepper; Lens (geology); Wavefront; Visualization; Computer science; Optics; Astigmatism; Python (programming language); 3D reconstruction; Super-resolution microscopy; Physics; Computer vision; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006183025,0.0009826215,0.0004523586,0.0003658236,0.0003520764,0.0009873431,0.0005441147,0.0006433017,0.001604449],"category_scores_gemma":[0.001823927,0.0004969264,0.0005067504,0.0005260624,0.000387109,0.0006429165,0.00121585,0.0006187959,0.0006587718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001287295,"about_ca_system_score_gemma":0.001584204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001655438,"about_ca_topic_score_gemma":0.003471033,"domain_scores_codex":[0.9994761,0.00008586155,0.00003050934,0.00005031723,0.0002963132,0.00006086226],"domain_scores_gemma":[0.9992686,0.0002650447,0.0001173403,0.0001158825,0.0001837294,0.0000494205],"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":[0.0003891579,0.000180832,0.003181192,0.0002370656,0.0001003716,0.0001460822,0.0002806592,0.3550618,0.5516928,0.01697982,0.001856752,0.06989347],"study_design_scores_gemma":[0.00003377286,0.00006375118,0.0007692659,0.00001524312,0.00001970391,0.0001289867,0.00003294766,0.8298943,0.1624002,0.004062878,0.002527754,0.00005119641],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1299147,0.000271989,0.8632967,0.0002988099,0.00003825569,0.00007123667,0.0002030808,0.001451639,0.00445363],"genre_scores_gemma":[0.5145311,0.0003357221,0.4820015,0.0001182915,0.00001359697,0.00009338569,0.0002549106,0.000738239,0.001913285],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001655438,"threshold_uncertainty_score":0.009340048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00716492753692665,"score_gpt":0.2777989830453382,"score_spread":0.2706340555084115,"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."}}