{"id":"W2036343126","doi":"10.1109/ciisp.2007.369181","title":"Human-Controlled Vs. Semi-automatic Content-Based Image Retrieval","year":2007,"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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Relevance feedback; Content-based image retrieval; Relevance (law); Image retrieval; Cluster analysis; Workload; Process (computing); Scheme (mathematics); Information retrieval; Data mining; Artificial intelligence; Machine learning; 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":[0.001437223,0.0001997484,0.0003594285,0.0002107335,0.0001898198,0.0002598841,0.0008969523,0.0001052093,0.0001708314],"category_scores_gemma":[0.0001938354,0.0001542774,0.0001953345,0.0005770283,0.0001004328,0.0004645124,0.00009565426,0.0001664853,0.0001612117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009045826,"about_ca_system_score_gemma":0.0000783896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001905241,"about_ca_topic_score_gemma":0.000003164083,"domain_scores_codex":[0.9980339,0.00007139899,0.0005889431,0.0003935238,0.00049461,0.0004176462],"domain_scores_gemma":[0.9984152,0.0001985948,0.0002073864,0.0007237012,0.0003049535,0.0001501216],"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":[0.0001381489,0.0002964982,0.0001927125,0.00004575936,0.00003773033,0.00005859467,0.00008053564,1.461562e-7,0.9192102,0.06805582,0.001349122,0.01053473],"study_design_scores_gemma":[0.003323971,0.0001896235,0.004100344,0.00002490356,0.0000140501,0.000008954858,0.00004015189,0.01920087,0.9710019,0.001000832,0.000792613,0.000301777],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01001876,0.00004503062,0.9748386,0.001444184,0.000130267,0.0005251587,9.770333e-7,0.001596303,0.01140071],"genre_scores_gemma":[0.8397546,0.000002725776,0.1515636,0.002058409,0.00007107835,0.00001645904,0.000004591234,0.00001821184,0.006510342],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8297359,"threshold_uncertainty_score":0.6291249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02911428002502182,"score_gpt":0.2877133785009311,"score_spread":0.2585990984759093,"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."}}