{"id":"W2095955803","doi":"10.1109/camp.1993.622480","title":"A smart buffer for tracking using motion data","year":2002,"lang":"en","type":"article","venue":"","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Computer vision; Optical flow; Artificial intelligence; Asynchronous communication; Stereopsis; Machine vision; Image processing; Neuromorphic engineering; Data processing; Multiprocessing; Real-time computing; Artificial neural network; Image (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":[0.000050194,0.00006171608,0.00006073821,0.00002870113,0.00003438886,0.00003626502,0.00009794432,0.00002242212,0.0001721463],"category_scores_gemma":[0.00001686381,0.00006051993,0.00001867063,0.00005253753,0.000006158259,0.0001957413,0.00001546646,0.00003876972,0.00002321057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001729763,"about_ca_system_score_gemma":7.806261e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001318889,"about_ca_topic_score_gemma":0.000007873148,"domain_scores_codex":[0.9996259,0.000002855632,0.0000811892,0.000107785,0.00004701974,0.0001352153],"domain_scores_gemma":[0.9996952,0.00001912805,0.000005789062,0.0002458622,0.00001140198,0.00002260151],"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":[0.000009006853,0.000158971,0.003937368,0.0004828493,0.0002025294,0.0000148299,0.001552309,0.08521023,0.1341849,0.001622121,0.115764,0.6568609],"study_design_scores_gemma":[0.0001312778,0.000002396586,0.000183811,0.000009733031,0.00001172006,0.000007046067,0.00002501544,0.9839996,0.002327961,0.00003656021,0.01317685,0.00008803778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4029625,0.0004064267,0.5815787,0.0002079763,0.0007442577,0.0002536053,0.0000382828,0.0009166942,0.01289152],"genre_scores_gemma":[0.9897405,0.000008245341,0.009642181,0.00004688808,0.0001205561,0.000001400033,0.0000149445,0.00002309758,0.0004021844],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8987893,"threshold_uncertainty_score":0.2467931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1416687497252557,"score_gpt":0.269154756882415,"score_spread":0.1274860071571593,"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."}}