{"id":"W2091897342","doi":"10.1145/1029949.1029956","title":"Visualizing 3D scenes using non-linear projections and data mining of previous camera movements","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Perspective (graphical); Generality; Computer vision; Projection (relational algebra); Artificial intelligence; Visualization; Flexibility (engineering); Computer graphics (images); Subdivision; Linear model; Algorithm; Mathematics; Machine learning; Geography","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":[0.000140806,0.00008572706,0.0001176614,0.00009461315,0.0001377389,0.00005660682,0.0004023712,0.00001585209,0.000003150585],"category_scores_gemma":[0.00003582029,0.00007622933,0.00001258785,0.0002755811,0.00003727606,0.001017698,0.0007010912,0.00004744807,0.000001340222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002007385,"about_ca_system_score_gemma":0.0000648917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001698525,"about_ca_topic_score_gemma":0.000009234493,"domain_scores_codex":[0.9991712,0.00001248019,0.0001996668,0.0003330808,0.0001271733,0.0001564232],"domain_scores_gemma":[0.999307,0.00001945599,0.00008403158,0.0004922873,0.00005110529,0.00004609951],"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":[0.00001759636,0.0006814197,0.01534529,0.0002519688,0.0001394946,0.00004181982,0.01033542,0.01333177,0.2428828,0.004115435,0.0001696776,0.7126873],"study_design_scores_gemma":[0.0004481041,0.00003915036,0.0001869918,0.0001539618,0.00000568344,0.00002556706,0.0002984837,0.9884204,0.009719969,0.0001060784,0.0004648433,0.0001307768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06753834,0.0001146757,0.9316733,0.00005931434,0.0001014651,0.0001186364,0.000001871194,0.0000440263,0.0003483255],"genre_scores_gemma":[0.2297135,0.00003196478,0.7699658,0.0002029935,0.0000223328,0.000001120694,0.000002508534,0.000006304775,0.00005348413],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9750886,"threshold_uncertainty_score":0.3108542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08597904122702214,"score_gpt":0.3991086913325115,"score_spread":0.3131296501054893,"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."}}