{"id":"W2164371126","doi":"10.1109/tpami.2005.139","title":"Generic model abstraction from examples","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Army Research Office; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Computer science; Adjacency list; Abstraction; Semantic gap; Artificial intelligence; Representation (politics); Theoretical computer science; Bridging (networking); Graph; Pattern recognition (psychology); Image (mathematics); Computer vision; Algorithm; Image retrieval","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":[0.001197889,0.0009965237,0.001257157,0.002565132,0.0009858739,0.0029801,0.002376432,0.001494686,0.0073605],"category_scores_gemma":[0.007078449,0.00071123,0.002211273,0.002238688,0.001497553,0.005286161,0.005131695,0.002514104,0.001449322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001185875,"about_ca_system_score_gemma":0.0008944699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002053536,"about_ca_topic_score_gemma":0.003590331,"domain_scores_codex":[0.9984112,0.0003428693,0.0001111604,0.0003749882,0.0005825363,0.0001773136],"domain_scores_gemma":[0.9974483,0.0009711033,0.0001611193,0.001020676,0.0002865803,0.0001122623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002591885,0.0001033024,0.002189659,0.0005985339,0.00008523867,0.0006107853,0.0009829852,0.1934497,0.007668957,0.4666145,0.0134341,0.3140032],"study_design_scores_gemma":[0.00002469325,0.00004581441,0.0003802397,0.00008118936,0.00003354943,0.0003824516,0.0003410869,0.5102367,0.003942513,0.4648198,0.01968494,0.00002710549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02031962,0.0002378738,0.9730563,0.0003058286,0.00001598805,0.00007968891,0.0003452834,0.0009097159,0.004729706],"genre_scores_gemma":[0.2867894,0.0005830229,0.7052726,0.0001663353,0.00004051313,0.0002177436,0.002996205,0.0004882291,0.003446007],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0073605,"threshold_uncertainty_score":0.02462327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03650880363853268,"score_gpt":0.2953794501682167,"score_spread":0.258870646529684,"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."}}