{"id":"W2087305436","doi":"10.1177/1063293x05053794","title":"Relevance-based Content Modeling and Object Retrieval from Multi-source Image Data","year":2005,"lang":"en","type":"article","venue":"Concurrent Engineering","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada; Lviv Polytechnic National University","keywords":"Computer science; Image retrieval; Artificial intelligence; Object (grammar); Set (abstract data type); Representation (politics); Information retrieval; Pattern recognition (psychology); Relevance (law); Feature (linguistics); Automatic image annotation; Data mining; Image (mathematics); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002230617,0.0007551684,0.001363122,0.003947617,0.0004303618,0.002055215,0.001719946,0.001332151,0.0007167616],"category_scores_gemma":[0.007437265,0.0005713133,0.001288612,0.002677552,0.001232151,0.00433245,0.001030937,0.0006924081,0.0006897062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001171778,"about_ca_system_score_gemma":0.0006496109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001846362,"about_ca_topic_score_gemma":0.001436373,"domain_scores_codex":[0.9978187,0.0005873651,0.0001491039,0.0004481967,0.0008686858,0.0001278237],"domain_scores_gemma":[0.997846,0.0009755286,0.0003237717,0.0004295754,0.0003905236,0.00003463466],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004292623,0.0002370161,0.002159852,0.0009336881,0.0002260717,0.0007202196,0.0007840156,0.221928,0.07508118,0.05582144,0.004056991,0.6376222],"study_design_scores_gemma":[0.0000209186,0.0001024468,0.001445286,0.00002599336,0.00005674921,0.0003494206,0.0001073431,0.95351,0.01579115,0.02552151,0.003026831,0.00004227689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008287489,0.0003723973,0.9903486,0.0001028446,0.0000136247,0.00004962272,0.00005435197,0.0004091045,0.0003620874],"genre_scores_gemma":[0.4410608,0.001103927,0.5546945,0.0001188811,0.000202538,0.0002936331,0.0006759634,0.0001797474,0.001669954],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003947617,"threshold_uncertainty_score":0.01179677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08450296283644737,"score_gpt":0.2774692730309511,"score_spread":0.1929663101945038,"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."}}