{"id":"W2380159924","doi":"","title":"The Moving Object Detection based on Local Hierarchical GMM","year":2009,"lang":"en","type":"article","venue":"Signal Processing","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"L'Alliance Boviteq","funders":"","keywords":"Mixture model; Artificial intelligence; Dispose pattern; Computer science; Pattern recognition (psychology); Block (permutation group theory); Object detection; Computer vision; Process (computing); Pixel; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003257413,0.000104469,0.00008342259,0.00004838223,0.0007868213,0.0002136399,0.0001043159,0.00005549638,0.00004198052],"category_scores_gemma":[0.00003248631,0.00006066054,0.00004393592,0.000186845,0.00007401562,0.00009410419,0.00000167653,0.0002611041,0.00003904216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006889623,"about_ca_system_score_gemma":0.00007045931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001250743,"about_ca_topic_score_gemma":0.0002483746,"domain_scores_codex":[0.9990492,0.00008364562,0.0001313076,0.0001902188,0.0002707574,0.0002748024],"domain_scores_gemma":[0.999569,0.00018479,0.00004598982,0.00009262012,0.00002521898,0.00008238709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001064539,0.000006143155,0.0004293298,0.000004091686,0.000001122614,0.000008970664,0.00004417275,0.02324063,0.0001676283,0.000002101533,0.00001757581,0.9759718],"study_design_scores_gemma":[0.0001579583,0.000280761,0.03906273,0.0000551525,0.000005695611,0.00001069452,0.00006327819,0.9568363,0.001163329,0.001099036,0.001151052,0.0001140032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7368258,0.001183399,0.1828875,0.003034233,0.0004583084,0.0002796466,0.000004936314,0.0004040854,0.07492206],"genre_scores_gemma":[0.9986927,0.000004121116,0.0002912948,0.0007349928,0.0002017393,4.855424e-8,0.000005433958,0.000002612607,0.00006705028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9758578,"threshold_uncertainty_score":0.6051673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008985493593931698,"score_gpt":0.2104376037058144,"score_spread":0.2014521101118827,"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."}}