{"id":"W2105560332","doi":"10.1109/ccece.2004.1349712","title":"Minimizing human-machine interactions in automatic image retrieval","year":2004,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Relevance (law); Computer science; Image retrieval; Identification (biology); Relevance feedback; Artificial intelligence; Image (mathematics); Pattern recognition (psychology); Class (philosophy); Information retrieval; Data mining; Computer vision","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":[0.0002239633,0.0001025536,0.0001225705,0.0002092528,0.00009904365,0.000154934,0.0004963234,0.00002703041,0.0000899782],"category_scores_gemma":[0.00006856462,0.00008996483,0.00005289097,0.0006428328,0.00003894528,0.0007915006,0.0001245171,0.0001732048,0.0001151531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001423404,"about_ca_system_score_gemma":0.00005038679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001052,"about_ca_topic_score_gemma":0.00003079593,"domain_scores_codex":[0.9990624,0.00003265731,0.0002947038,0.0002502769,0.0001750126,0.0001849352],"domain_scores_gemma":[0.9993874,0.00004758528,0.00007092987,0.0003858424,0.00005673702,0.0000515482],"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":[0.000009906021,0.000768712,0.0004711623,0.00006983109,0.00002087192,0.00009312949,0.001743675,0.00001096158,0.5756816,0.389451,0.0004482817,0.03123094],"study_design_scores_gemma":[0.001151401,0.0001115412,0.01034409,0.0001524325,0.0000069311,0.00007393487,0.0001617914,0.03055834,0.9107268,0.04449186,0.00170515,0.000515715],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01116249,0.00003057617,0.9697407,0.003253072,0.0001029697,0.0001504375,7.312573e-7,0.0007848563,0.01477414],"genre_scores_gemma":[0.7737553,0.000006234778,0.2253413,0.0002631055,0.00001729544,0.000007707086,0.000002336819,0.000006480087,0.0006002985],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7625927,"threshold_uncertainty_score":0.3668659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02601249100340657,"score_gpt":0.3161179932970872,"score_spread":0.2901055022936806,"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."}}