{"id":"W2170751640","doi":"10.1109/icassp.2004.1326582","title":"Interactive image retrieval by query fusion","year":2004,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Feature (linguistics); Information retrieval; Image retrieval; Query expansion; Point (geometry); Similarity (geometry); Space (punctuation); Feature vector; Image (mathematics); Data mining; Artificial intelligence; 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":[0.0001454024,0.00009611753,0.00008873156,0.00005899887,0.00007919263,0.0001504819,0.0004995831,0.00005111681,0.00006655372],"category_scores_gemma":[0.00004619099,0.00007752638,0.00005134386,0.0003468337,0.00004491469,0.001203743,0.0001657046,0.0001260159,0.000236617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009964746,"about_ca_system_score_gemma":0.0000547834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003402719,"about_ca_topic_score_gemma":9.813715e-7,"domain_scores_codex":[0.9991652,0.00002522926,0.0001570716,0.000277141,0.0002147247,0.0001606018],"domain_scores_gemma":[0.9993761,0.00003207676,0.00006179738,0.0003459404,0.0001181836,0.00006591967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002342609,0.0001621312,0.000009181901,0.000006118461,0.000007309868,0.00001112169,0.0002945039,1.272322e-7,0.8972042,0.07722692,0.006479517,0.01857549],"study_design_scores_gemma":[0.0001875871,0.00007488872,0.0001072428,0.00001090673,0.000001332078,0.00001112466,0.00002835623,0.0003007976,0.9780005,0.01278189,0.008369206,0.0001261308],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001780034,0.00004183562,0.9771888,0.003680138,0.0001135503,0.0001076746,0.000001787578,0.0005934685,0.01649272],"genre_scores_gemma":[0.7605832,0.00008113515,0.2306896,0.001623595,0.00006076485,0.000009116168,0.000009914584,0.00001448791,0.006928106],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7588032,"threshold_uncertainty_score":0.3161434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007919846383710552,"score_gpt":0.2540241948254517,"score_spread":0.2461043484417412,"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."}}