{"id":"W2950572451","doi":"10.1038/s41598-018-37767-1","title":"MTrack: Automated Detection, Tracking, and Analysis of Dynamic Microtubules","year":2019,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Microtubule and mitosis dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Australian National University; Humboldt-Universität zu Berlin; McGill University; Deutsche Forschungsgemeinschaft; Yale University","keywords":"Microtubule; Computer science; Benchmark (surveying); Noise (video); Data mining; Pixel; Tracking (education); Tubulin; Population; Biological system; Artificial intelligence; Computer vision; Biology; Image (mathematics); Cartography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001450135,0.00138836,0.00100327,0.002571227,0.001020878,0.00153611,0.002013999,0.001104332,0.005603454],"category_scores_gemma":[0.003277168,0.0007256409,0.0007396138,0.001467866,0.0004781366,0.001384017,0.001396061,0.001264711,0.004256383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005826672,"about_ca_system_score_gemma":0.001347232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002212935,"about_ca_topic_score_gemma":0.003093773,"domain_scores_codex":[0.9985084,0.000109658,0.0001107474,0.0004108997,0.0007210531,0.0001392487],"domain_scores_gemma":[0.9981949,0.0003706438,0.000309536,0.0005063935,0.0004740904,0.0001445541],"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":[0.0009753679,0.0002814493,0.006064743,0.001179323,0.0003145585,0.0003426436,0.0003240402,0.01156545,0.5391886,0.00317214,0.07112749,0.3654642],"study_design_scores_gemma":[0.0001608762,0.0003189404,0.02772296,0.0001345882,0.00009982536,0.001789437,0.00009038032,0.3578451,0.5398154,0.00361071,0.06801443,0.0003972784],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0755441,0.001405679,0.762944,0.0003992956,0.0002412998,0.0003059124,0.01156193,0.1442482,0.003349517],"genre_scores_gemma":[0.1504448,0.0007445624,0.8190129,0.0002404574,0.0001073466,0.0007294159,0.01625178,0.007523566,0.0049451],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005603454,"threshold_uncertainty_score":0.01874542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003947245256503101,"score_gpt":0.2348284650730293,"score_spread":0.2308812198165262,"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."}}