{"id":"W4252419279","doi":"10.1057/palgrave.ivs.9500005","title":"Filtering and Brushing with Motion","year":2002,"lang":"en","type":"article","venue":"Information Visualization","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Colligo (Canada); Simon Fraser University","funders":"","keywords":"Computer science; Motion (physics); Pairwise comparison; Visualization; Computer vision; Filter (signal processing); Structure from motion; Artificial intelligence; Perception; Vocabulary; Human–computer interaction","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":[0.001420931,0.000952439,0.001420638,0.001416565,0.0009376929,0.002087683,0.001143087,0.001122122,0.007585023],"category_scores_gemma":[0.01134935,0.0006855772,0.001244762,0.001095155,0.001031622,0.002969431,0.002141206,0.0009440577,0.0009917491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004956899,"about_ca_system_score_gemma":0.0003707632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001405534,"about_ca_topic_score_gemma":0.001269281,"domain_scores_codex":[0.9990523,0.0002163978,0.0001085915,0.0002001134,0.0003117901,0.0001106833],"domain_scores_gemma":[0.9965739,0.002071275,0.0002208926,0.0006108473,0.0003745606,0.0001485202],"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":[0.0009381174,0.0001173981,0.002039809,0.001292634,0.0001417275,0.0004118185,0.002035246,0.01953379,0.282546,0.02238779,0.003850353,0.6647053],"study_design_scores_gemma":[0.0004949034,0.002544922,0.01496531,0.0009455676,0.0006203858,0.002522779,0.001838814,0.4501563,0.2988607,0.0887161,0.1378559,0.0004784164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09609464,0.002380381,0.8899978,0.0006057059,0.0004518781,0.0003533435,0.0001376657,0.002580515,0.007398048],"genre_scores_gemma":[0.4499257,0.002160721,0.5403181,0.000527349,0.0001863875,0.0003295695,0.0002538946,0.0005674474,0.005730772],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007585023,"threshold_uncertainty_score":0.02537441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01898284192553616,"score_gpt":0.2502139247567247,"score_spread":0.2312310828311886,"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."}}