{"id":"W1992606915","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":71,"is_retracted":false,"has_abstract":false,"ca_institutions":"Colligo (Canada)","funders":"","keywords":"Computer science; Motion (physics); Computer vision; Artificial intelligence; Computer graphics (images); Data science","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":[0.001127337,0.001433894,0.002055117,0.002374287,0.001218245,0.003356704,0.001210853,0.001331876,0.01940707],"category_scores_gemma":[0.006428422,0.0008912091,0.001724394,0.00209819,0.0008293375,0.002885372,0.002112738,0.001595374,0.002844697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005737717,"about_ca_system_score_gemma":0.000788545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003636503,"about_ca_topic_score_gemma":0.003458739,"domain_scores_codex":[0.9992439,0.0001017768,0.00007659553,0.0002050547,0.000259144,0.0001135812],"domain_scores_gemma":[0.9978282,0.000793973,0.0001210382,0.0006463968,0.0004295005,0.0001808128],"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":[0.0004728906,0.0001332588,0.001070465,0.0005838819,0.00008511345,0.0001829319,0.0005893855,0.01196069,0.09920767,0.0287187,0.009532609,0.8474624],"study_design_scores_gemma":[0.0001853457,0.0004363775,0.004100445,0.000230321,0.0002037275,0.0007638517,0.0004722708,0.6586255,0.1697171,0.08222565,0.0828636,0.0001756512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0134512,0.0005071659,0.9780762,0.0002002351,0.0002684342,0.00009382985,0.0001826168,0.00505431,0.002166015],"genre_scores_gemma":[0.1517391,0.001038871,0.8337454,0.0001984307,0.0002013058,0.0001781539,0.0006903397,0.002068121,0.01014022],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01940707,"threshold_uncertainty_score":0.06492311,"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."}}