{"id":"W2044801298","doi":"10.1155/2010/582760","title":"Multilevel Space‐Time Aggregation for Bright Field Cell Microscopy Segmentation and Tracking","year":2010,"lang":"en","type":"article","venue":"International Journal of Biomedical Imaging","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Ontario Institute for Cancer Research; McGill University and Génome Québec Innovation Centre; University of Waterloo","funders":"","keywords":"Segmentation; Computer science; Tracking (education); Field (mathematics); Microscopy; Space (punctuation); Data mining; Computer vision; Data science; Artificial intelligence; Optics; Physics; Mathematics","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":[0.0005127882,0.0003720384,0.0006253485,0.0008540949,0.0006009901,0.000854243,0.0006290112,0.0007462796,0.0010482],"category_scores_gemma":[0.001247293,0.0002631254,0.0007249117,0.0009356644,0.0005662968,0.0006406878,0.0008644892,0.0008057058,0.0003344782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008347235,"about_ca_system_score_gemma":0.0006614509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002338676,"about_ca_topic_score_gemma":0.002530342,"domain_scores_codex":[0.9996375,0.00007008713,0.00001898334,0.00006224055,0.0001708135,0.00004042001],"domain_scores_gemma":[0.999474,0.0002207399,0.00008707934,0.00008751568,0.00009097119,0.00003964742],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001704928,0.00006227048,0.00143093,0.0002627081,0.00008150737,0.0002893587,0.0005553014,0.3312912,0.2892945,0.05190031,0.002329125,0.3223323],"study_design_scores_gemma":[0.000005714251,0.00004759331,0.0005955511,0.000007907271,0.00001255042,0.0001148095,0.00002615041,0.9705084,0.01806639,0.006881343,0.003715729,0.000017742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007455349,0.0001284257,0.9917509,0.00006506268,0.00001815429,0.00001242899,0.00001224501,0.0001687945,0.0003885828],"genre_scores_gemma":[0.1589122,0.0002842426,0.8394407,0.00003765455,0.00004883943,0.00008103698,0.0000727382,0.00008536899,0.001037164],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002338676,"threshold_uncertainty_score":0.006056309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005168632311982556,"score_gpt":0.2860913871067526,"score_spread":0.28092275479477,"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."}}