{"id":"W2009605648","doi":"10.1117/12.527262","title":"Global semantic classification of scenes using ridgelet transform","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Computer Research Institute of Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Curvelet; Artificial intelligence; Sparse approximation; Pattern recognition (psychology); Focus (optics); Representation (politics); Dimension (graph theory); Fourier transform; Computer vision; Filter (signal processing); Shearlet; Image (mathematics); Wavelet transform; Mathematics; Wavelet","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003277809,0.0003345715,0.0004534456,0.0001117123,0.00005876012,0.00004957872,0.0007471478,0.0002067424,0.00000667705],"category_scores_gemma":[0.0002043093,0.000306713,0.0005230404,0.0005077568,0.0002384799,0.0006629935,0.00007403362,0.000222252,7.10496e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003893051,"about_ca_system_score_gemma":0.00003594106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001522312,"about_ca_topic_score_gemma":4.222399e-7,"domain_scores_codex":[0.9979021,8.475965e-9,0.0008208128,0.0002960146,0.0006091953,0.0003718464],"domain_scores_gemma":[0.9983412,0.00004629414,0.0002750322,0.00007191846,0.001166961,0.00009860343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003615987,0.00007982007,0.0002257255,0.0007408412,0.0002023014,7.406533e-8,0.0001042903,0.003694333,0.7809572,0.2128991,0.0003081825,0.0007519466],"study_design_scores_gemma":[0.001295883,0.0002333231,0.00184152,0.0007988192,0.000197665,0.00003450272,0.0008465998,0.1078147,0.8763253,0.009292687,0.0007961228,0.0005229043],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9911926,0.0001487959,0.005412341,0.0005062761,0.000191629,0.0005677096,0.00007122242,0.000274844,0.001634607],"genre_scores_gemma":[0.7596657,0.0001525948,0.2398709,0.00001948991,0.0001506621,0.00006079572,0.000006916235,0.0000601425,0.00001279263],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2344586,"threshold_uncertainty_score":0.9999385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01390903779769868,"score_gpt":0.2494315032030912,"score_spread":0.2355224654053926,"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."}}