{"id":"W2093872708","doi":"10.1155/ijbi/2006/12186","title":"Probabilistic Model‐Based Cell Tracking","year":2006,"lang":"en","type":"article","venue":"International Journal of Biomedical Imaging","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; University of Waterloo","keywords":"Computer science; Probabilistic logic; Tracking (education); Data mining; Statistical model; Data science; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009295758,0.0005474532,0.001153589,0.0006692606,0.0004155794,0.001131902,0.001716461,0.001419946,0.00165925],"category_scores_gemma":[0.00302507,0.00060423,0.001036152,0.0009626678,0.0007289363,0.001039569,0.001183688,0.0008714549,0.0007646875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009177944,"about_ca_system_score_gemma":0.0009285888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005019818,"about_ca_topic_score_gemma":0.003522699,"domain_scores_codex":[0.9994212,0.0001165654,0.00002460048,0.0001456843,0.0002509431,0.00004112069],"domain_scores_gemma":[0.9987375,0.0007584263,0.0001354943,0.0001512789,0.0001800496,0.0000372693],"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":[0.00005166813,0.00001744985,0.0007628641,0.00005557388,0.00003344721,0.00007162417,0.0000403082,0.9392285,0.005855833,0.01624046,0.001097155,0.03654517],"study_design_scores_gemma":[0.000004110104,0.000006774155,0.00008298012,0.00000205319,0.000004548464,0.00002243631,0.000001259286,0.9950146,0.0007241187,0.00343753,0.0006946884,0.000004798742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003547613,0.0001703547,0.9950681,0.00008744622,0.00001910948,0.00001257588,0.00005749983,0.0003006232,0.0007366365],"genre_scores_gemma":[0.5504947,0.00160467,0.437878,0.0002449533,0.0001207352,0.0003835759,0.0008834479,0.0002261447,0.008163856],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005019818,"threshold_uncertainty_score":0.009981215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006112849503138805,"score_gpt":0.2516506741579548,"score_spread":0.245537824654816,"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."}}