{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001160595,0.0002014431,0.0002454445,0.0003493055,0.0001687475,0.0001190586,0.0003721663,0.00006455142,0.00009058001],"category_scores_gemma":[0.000002813528,0.0001789301,0.0001913901,0.0007481224,0.00005139178,0.00062453,0.000005682835,0.0002316185,0.00003750625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004187358,"about_ca_system_score_gemma":0.00001380375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009605481,"about_ca_topic_score_gemma":0.0006918123,"domain_scores_codex":[0.9986867,0.00003484184,0.0003269921,0.0005239577,0.0002290143,0.0001985446],"domain_scores_gemma":[0.9991425,0.00009441604,0.00009963196,0.0004966687,0.00005897831,0.0001078407],"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":[0.000004666712,0.00007947406,0.00004251178,0.000001936871,0.0001198413,0.000002134648,0.0001488782,0.09305383,0.001946483,0.00003564926,0.000007525324,0.904557],"study_design_scores_gemma":[0.00003209655,0.00003509442,0.0002497927,0.000005780169,0.0001266086,0.000003261435,0.000007627907,0.5905487,0.4075491,0.001081239,0.0001948582,0.0001657988],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003332537,0.0002274052,0.99572,0.0002694887,0.00005627191,0.00007851082,0.00004014468,0.0002078127,0.00006781146],"genre_scores_gemma":[0.9224049,0.00132827,0.07545204,0.000585188,0.00003066849,0.00001585361,0.000005726713,0.000009610593,0.0001677186],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.920268,"threshold_uncertainty_score":0.7296556,"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."}}