{"id":"W1572819060","doi":"","title":"Weighted pseudo-metric for a fast CBIR method","year":2006,"lang":"en","type":"article","venue":"International Conference on Computer Vision and Graphics","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Metric (unit); Logistic regression; Pattern recognition (psychology); Logistic model tree; Computer science; Artificial intelligence; Image retrieval; Feature (linguistics); Similarity (geometry); Measure (data warehouse); Mathematics; Feature extraction; Regression analysis; Image (mathematics); Data mining; Machine learning","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":[0.0003236524,0.000175238,0.0001660764,0.0005140089,0.0001352876,0.0004736351,0.0007029055,0.00009336904,0.00001758449],"category_scores_gemma":[0.00001424366,0.000143312,0.0001029459,0.0004711353,0.00005543697,0.0003086868,0.000159068,0.0001442561,0.00001050318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000224888,"about_ca_system_score_gemma":0.00003849788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002121993,"about_ca_topic_score_gemma":0.000003021806,"domain_scores_codex":[0.9986134,0.00005879751,0.0002955608,0.000473807,0.0003847496,0.0001736434],"domain_scores_gemma":[0.9987935,0.0002117005,0.0001385116,0.0002628825,0.000524411,0.00006894251],"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":[0.00002112214,0.00009018646,0.00003755534,0.000005892362,0.00001074653,0.000002480579,0.000017763,0.000001679144,0.0006051582,0.7994307,0.001537679,0.198239],"study_design_scores_gemma":[0.0004403196,0.0003242563,0.002819654,0.00004229999,0.000004190757,0.00002103905,0.000003453705,0.8455349,0.003046181,0.1260543,0.02150052,0.0002089775],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004781024,0.00002662242,0.9914015,0.004885803,0.0004535598,0.0002062706,0.00001599091,0.000229837,0.002302326],"genre_scores_gemma":[0.4924201,0.0001306945,0.5049551,0.001450424,0.0002528492,0.00004407689,0.00004373209,0.00001324212,0.0006898415],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8455332,"threshold_uncertainty_score":0.5844094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02933878541128848,"score_gpt":0.3330012633302475,"score_spread":0.303662477918959,"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."}}