{"id":"W3126161118","doi":"10.1023/b:visi.0000027789.58057.14","title":"Learning Generative Models of Scene Features","year":2004,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Generative model; Measure (data warehouse); A priori and a posteriori; Probabilistic logic; Pattern recognition (psychology); Visualization; Object (grammar); Salient; Set (abstract data type); Statistical model; Generative grammar; Data mining","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":[0.001225266,0.001023427,0.001423395,0.001978626,0.0004801302,0.001768923,0.002484929,0.002677307,0.003223474],"category_scores_gemma":[0.005655981,0.002146787,0.002755581,0.001654456,0.001568282,0.00256826,0.00181763,0.003196833,0.001355007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001405412,"about_ca_system_score_gemma":0.0007268795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006660323,"about_ca_topic_score_gemma":0.01462373,"domain_scores_codex":[0.9992691,0.0002238923,0.00002679307,0.0002570105,0.000134504,0.00008876296],"domain_scores_gemma":[0.9958832,0.003050244,0.0002945666,0.0004423241,0.0002054655,0.0001242125],"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":[0.0002370428,0.0001683574,0.006291438,0.0002050809,0.0003129292,0.0004123767,0.0004129349,0.8024851,0.007181293,0.074591,0.004009841,0.1036926],"study_design_scores_gemma":[0.00001665231,0.00001896861,0.0005054045,0.00001336134,0.00002948537,0.00008959552,0.00001501617,0.9758123,0.0005081872,0.02255446,0.0004223466,0.00001417487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05289833,0.0005759151,0.9424394,0.0005602594,0.00009178287,0.00005141183,0.000428503,0.001334312,0.001620123],"genre_scores_gemma":[0.8821914,0.00115906,0.1056783,0.0004701371,0.000249488,0.0002197688,0.001871221,0.0006067188,0.007553943],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006660323,"threshold_uncertainty_score":0.01324308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01519103776733947,"score_gpt":0.2978155752803361,"score_spread":0.2826245375129967,"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."}}