{"id":"W2348677414","doi":"","title":"Query Relaxation and Answer Integration Based on Agent for Cross-Media Meta-Searches","year":2007,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Search engine; Information retrieval; Relaxation (psychology); Recall; Precision and recall; Metasearch engine; Data mining; Web search query","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002662935,0.0008089519,0.001539964,0.001422343,0.0007713204,0.001669199,0.002211745,0.001495638,0.002984961],"category_scores_gemma":[0.005971085,0.0006565815,0.001034437,0.001355721,0.0007850848,0.00452762,0.001815633,0.001889377,0.001000693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007543424,"about_ca_system_score_gemma":0.001267706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003808266,"about_ca_topic_score_gemma":0.003825031,"domain_scores_codex":[0.9974321,0.001010997,0.0002814047,0.0003398542,0.0007731781,0.0001623717],"domain_scores_gemma":[0.9965844,0.001672607,0.0002213327,0.0006338213,0.0007414505,0.0001463615],"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":[0.00257651,0.001931421,0.003392325,0.0009256165,0.0004588225,0.0005548609,0.002201325,0.05334226,0.1199067,0.04529331,0.01012639,0.7592905],"study_design_scores_gemma":[0.0003371101,0.0005080492,0.0009986443,0.00004743008,0.0002908663,0.0004675858,0.0003291239,0.9223518,0.04547373,0.0120934,0.01697816,0.0001241341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03873371,0.001183353,0.951454,0.0004406749,0.0000715698,0.0004364862,0.00009863482,0.003731323,0.003850291],"genre_scores_gemma":[0.2887504,0.0003781838,0.7055141,0.0002941299,0.00009452685,0.0004315279,0.0004057407,0.0002256762,0.003905779],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003808266,"threshold_uncertainty_score":0.01408315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05275532356669788,"score_gpt":0.3140653571825278,"score_spread":0.2613100336158299,"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."}}