{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001021711,0.0001961933,0.000215394,0.000239115,0.0004900811,0.0000633062,0.0002064336,0.0001377452,0.0001171071],"category_scores_gemma":[0.00003362366,0.0002072882,0.000199236,0.000966551,0.00005353239,0.00009818152,0.000003061278,0.0003478506,0.00006326911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000043661,"about_ca_system_score_gemma":0.000011052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004540829,"about_ca_topic_score_gemma":0.0002488164,"domain_scores_codex":[0.9990306,0.00004092414,0.0002423299,0.0002288115,0.0001862329,0.0002710851],"domain_scores_gemma":[0.9992128,0.0001356411,0.00002033136,0.0004705862,0.00007178274,0.00008885327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002848141,0.0002948234,0.00008021181,0.00006289645,0.0008410224,0.000008644141,0.0009682555,0.9655105,0.002144156,0.0006603072,0.001289988,0.02811071],"study_design_scores_gemma":[0.001172154,0.0002672738,0.00004708667,0.00009032935,0.0004295243,0.0000743077,0.0002694579,0.9569517,0.02339189,0.01059286,0.005867809,0.0008456447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03827919,0.00008500447,0.9595188,0.0003263711,0.0003287369,0.00007118892,0.00002248286,0.0006022642,0.0007659047],"genre_scores_gemma":[0.9947582,0.0002450504,0.004337652,0.0003636442,0.00006199117,0.0000177452,0.000007975584,0.00005670942,0.000151042],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.956479,"threshold_uncertainty_score":0.8452969,"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."}}