{"id":"W73345698","doi":"10.1007/3-540-32367-8_1","title":"Human-Centered Computing for Image and Video Retrieval","year":2006,"lang":"en","type":"book-chapter","venue":"Studies in fuzziness and soft computing","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Relevance (law); Relevance feedback; Video retrieval; Information retrieval; Image retrieval; Extension (predicate logic); Multimedia; Image (mathematics); Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007411862,0.0004435437,0.0006819807,0.0005432573,0.0004824607,0.001789191,0.00093188,0.000785493,0.008607671],"category_scores_gemma":[0.00208252,0.0001722145,0.0003781504,0.001374258,0.001178189,0.002176716,0.0007840893,0.001144307,0.001465381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007137059,"about_ca_system_score_gemma":0.0006017442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001325269,"about_ca_topic_score_gemma":0.002344048,"domain_scores_codex":[0.9995189,0.0001473328,0.00002037031,0.00007546705,0.0002142772,0.00002370312],"domain_scores_gemma":[0.9993287,0.0003966146,0.0000250466,0.000106853,0.0001187771,0.00002403247],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005816248,0.00005245109,0.0002703151,0.0005373717,0.0000568107,0.00007788338,0.0005020032,0.01023814,0.005145826,0.3644002,0.02952906,0.5891317],"study_design_scores_gemma":[0.00001394419,0.00010075,0.001166409,0.0002164501,0.00004128687,0.0003989858,0.0002663352,0.1300932,0.007052175,0.7259367,0.1346694,0.00004434312],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007792659,0.03684688,0.9105049,0.003016719,0.0005790432,0.0001136128,0.00008929058,0.000744726,0.04031217],"genre_scores_gemma":[0.2981922,0.02459713,0.6167814,0.001046941,0.00121464,0.00029692,0.0002389152,0.0002807186,0.05735114],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008607671,"threshold_uncertainty_score":0.02879548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06244865756370251,"score_gpt":0.3306865275534064,"score_spread":0.2682378699897039,"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."}}