{"id":"W2973350939","doi":"10.1101/777722","title":"FOCAL3D: A 3-dimensional clustering package for single-molecule localization microscopy","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada","keywords":"DBSCAN; Cluster analysis; Computer science; Python (programming language); Microscopy; Algorithm; Data mining; Pattern recognition (psychology); Artificial intelligence; Fuzzy clustering; Physics; CURE data clustering algorithm; Optics","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":[0.0003304171,0.0006756369,0.0005202729,0.0001587724,0.0001679028,0.0001560851,0.0006108949,0.001066115,0.000009057813],"category_scores_gemma":[0.0002550803,0.0007854463,0.0002505562,0.0001785167,0.0001658251,0.00001586076,0.0009304672,0.0004242343,0.00001968609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002029332,"about_ca_system_score_gemma":0.0004101303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008533354,"about_ca_topic_score_gemma":0.000002353373,"domain_scores_codex":[0.996973,0.00009093246,0.0005529759,0.001447394,0.0002512421,0.0006844241],"domain_scores_gemma":[0.9972641,0.00002746235,0.0004339679,0.001510883,0.0005802743,0.0001832449],"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":[0.0001391611,0.0001266898,0.0005137637,0.0003966634,0.00008049542,0.000006632078,0.000002801293,0.0008073606,0.9961693,0.00002381086,0.001731682,0.000001633642],"study_design_scores_gemma":[0.000558423,0.000276447,0.0001970419,0.0003993639,0.00006129333,6.338922e-8,0.000001330327,0.001555012,0.9901902,0.000003548605,0.005894131,0.0008631301],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1549501,0.001249642,0.8404252,0.00005629304,0.000876605,0.001696155,0.0004981905,0.0002397766,0.000008025472],"genre_scores_gemma":[0.8532105,0.0001188816,0.144823,0.0006194288,0.0004787973,0.0004091325,0.00002065679,0.0002986899,0.00002087985],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6982604,"threshold_uncertainty_score":0.9994596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01081338464544965,"score_gpt":0.2532098774944611,"score_spread":0.2423964928490114,"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."}}