{"id":"W2015466769","doi":"10.1145/2601097.2601109","title":"Organizing heterogeneous scene collections through contextual focal points","year":2014,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Israel Science Foundation; Ministry of Science and Technology of the People's Republic of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Focal point; Cluster analysis; Computer science; Focal length; Artificial intelligence; Cardinal point; Cluster (spacecraft); Perspective (graphical); Set (abstract data type); Computer vision; Point (geometry); Pattern recognition (psychology); Mathematics; Lens (geology)","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":[0.0009628976,0.001018296,0.001568647,0.009533231,0.001790421,0.003656065,0.001822765,0.0009781598,0.001739516],"category_scores_gemma":[0.003957429,0.0007677507,0.001379227,0.007757662,0.001702084,0.004022998,0.004930587,0.0008327588,0.0005749733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001397223,"about_ca_system_score_gemma":0.001231401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009481278,"about_ca_topic_score_gemma":0.0146194,"domain_scores_codex":[0.9985163,0.0002148046,0.00007740004,0.0004883338,0.0004379933,0.0002652124],"domain_scores_gemma":[0.9981963,0.0004828004,0.0002750966,0.0004203559,0.0004783484,0.0001471091],"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":[0.0005212682,0.0003006594,0.0240206,0.0007671457,0.0004314905,0.001131683,0.004819014,0.1227652,0.05179147,0.08172509,0.006828753,0.7048976],"study_design_scores_gemma":[0.00006015714,0.0003450496,0.03115426,0.0002304498,0.0003697179,0.001799118,0.006745439,0.7256131,0.0284729,0.1669636,0.03802526,0.0002209617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04937865,0.0005031525,0.9462788,0.0001163297,0.0000173286,0.000227452,0.0003493165,0.0008724027,0.002256538],"genre_scores_gemma":[0.3637272,0.0006565402,0.6316711,0.0000884085,0.00005916872,0.0003473694,0.001714481,0.0003092471,0.001426435],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009533231,"threshold_uncertainty_score":0.01885217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01874825591911866,"score_gpt":0.2243885211860844,"score_spread":0.2056402652669658,"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."}}