{"id":"W2006326991","doi":"10.1007/s007780100059","title":"Efficient retrieval of similar shapes","year":2002,"lang":"en","type":"article","venue":"The VLDB Journal","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of Alberta","funders":"","keywords":"Search engine indexing; Normalization (sociology); Computer science; Pattern recognition (psychology); Artificial intelligence; Similarity (geometry); Invariant (physics); Image retrieval; Computer vision; Mathematics; Algorithm; Image (mathematics)","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":[0.0006531224,0.0000717366,0.0001014374,0.00007173125,0.0001855115,0.00009579099,0.0009597921,0.00003121584,0.0001712961],"category_scores_gemma":[0.00008080333,0.00004174856,0.00008996567,0.0003968528,0.00008381856,0.00008217354,0.00009540265,0.0002181682,0.00004550251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002347937,"about_ca_system_score_gemma":0.00001888456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.766324e-7,"about_ca_topic_score_gemma":3.380321e-8,"domain_scores_codex":[0.9990247,0.00008568938,0.0002493031,0.00009838912,0.0003835557,0.0001583711],"domain_scores_gemma":[0.99928,0.0000746016,0.0001860824,0.0002529551,0.000149643,0.00005674397],"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":[0.0001135992,0.001273541,0.0005167194,0.00006985281,0.0002055051,0.0001234285,0.01013988,0.001173468,0.2872528,0.1373526,0.03148193,0.5302967],"study_design_scores_gemma":[0.0005798488,0.0003358964,0.003812732,0.00008213458,0.00002893214,0.0008990161,0.0001234778,0.6736659,0.2880214,0.01261567,0.01950936,0.0003256486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03985538,0.001708141,0.9432848,0.009464792,0.0002772679,0.000123951,0.000001631209,0.0001367651,0.005147256],"genre_scores_gemma":[0.9923286,0.0001930014,0.006628607,0.0002188408,0.00009857462,5.218289e-7,4.245384e-8,0.000004710866,0.000527109],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9524732,"threshold_uncertainty_score":0.1875572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02979951787720897,"score_gpt":0.2431261256838491,"score_spread":0.2133266078066401,"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."}}