{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004521178,0.0001163604,0.0002111457,0.0002414688,0.0000797132,0.00006916236,0.00008440093,0.0001155518,0.00003234075],"category_scores_gemma":[0.00004267243,0.0001110545,0.0001414728,0.0004977784,0.0001429203,0.00000670402,0.00006745211,0.00004611805,0.000004142817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001074486,"about_ca_system_score_gemma":0.00005238794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002137164,"about_ca_topic_score_gemma":0.000209627,"domain_scores_codex":[0.9986977,0.00002733074,0.0003616124,0.0006009677,0.0001427241,0.000169652],"domain_scores_gemma":[0.998871,0.000005507933,0.0002497754,0.0006516532,0.0001671752,0.00005484789],"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":[0.000007142762,0.00003239737,0.02156047,0.00001745342,0.0002362623,0.000005232425,0.00003983766,0.0001208064,0.9764434,0.000004836887,0.0002225431,0.001309677],"study_design_scores_gemma":[0.0001569036,0.00007531988,0.06542223,0.00001466252,0.000353967,0.0001128841,0.00007147735,0.008997819,0.9151414,0.00008790178,0.009327736,0.0002376691],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976996,0.0004486821,0.0005678703,0.000007954446,0.0008706096,0.0001815237,0.00001115427,0.00003320716,0.0001794307],"genre_scores_gemma":[0.9978994,0.00002717354,0.0003179796,0.00001436608,0.000009180001,0.000004633953,0.0002722254,0.00001190941,0.001443107],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06130191,"threshold_uncertainty_score":0.4528672,"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."}}