{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007564711,0.0001417802,0.0001676582,0.0002365323,0.000248141,0.0003051748,0.0004238868,0.00006580401,0.000005362378],"category_scores_gemma":[0.00000809359,0.0001205232,0.000122975,0.0003912713,0.00005584221,0.0002593675,0.00008094979,0.0001017122,0.00002575738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004347456,"about_ca_system_score_gemma":0.00003606556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001841127,"about_ca_topic_score_gemma":0.00002454834,"domain_scores_codex":[0.9988263,0.00002926423,0.0002726106,0.0004960712,0.0001599634,0.0002157654],"domain_scores_gemma":[0.9987226,0.0004653556,0.0001074909,0.0004787197,0.0001440014,0.00008181724],"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":[0.00001671394,0.00025673,0.0006006338,0.00003426997,0.0001713987,0.000001256221,0.0007889794,0.0007411891,0.006453208,0.02451549,0.004704387,0.9617158],"study_design_scores_gemma":[0.001199836,0.0001809697,0.03198531,0.00004683203,0.0003400212,0.000009763403,0.00007472366,0.4702229,0.04054483,0.005040803,0.4496137,0.0007402685],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003312248,0.00006723518,0.9950301,0.0008748024,0.00001960983,0.0004051768,0.00003293461,0.0001197335,0.0001381457],"genre_scores_gemma":[0.2018758,0.00000570457,0.7962182,0.0009948801,0.0001619782,0.0003862697,0.0002232659,0.00001174716,0.0001222069],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9609755,"threshold_uncertainty_score":0.4914794,"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."}}