{"id":"W1981096766","doi":"10.1080/13614560208914740","title":"Searching the hypermedia Web: improved topic distillation through network analytic relevance ranking","year":2002,"lang":"en","type":"article","venue":"New Review of Hypermedia and Multimedia","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"","keywords":"Relevance (law); Ranking (information retrieval); Computer science; Information retrieval; Set (abstract data type); Hypermedia; Hyperlink; Variance (accounting); Regression analysis; Search engine; Regression; Network analysis; Web page; Statistics; Machine learning; World Wide Web; Mathematics; Engineering","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":[0.003934536,0.0008516033,0.001359633,0.00649031,0.0006061479,0.001806499,0.001318003,0.0007114618,0.001782179],"category_scores_gemma":[0.02277616,0.0003472309,0.0007578076,0.004013492,0.000560175,0.002743205,0.001337751,0.0009686752,0.0005909782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001106098,"about_ca_system_score_gemma":0.001248167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006229585,"about_ca_topic_score_gemma":0.006534364,"domain_scores_codex":[0.9965422,0.002117716,0.0001462099,0.0003283128,0.0007641379,0.0001014448],"domain_scores_gemma":[0.9863441,0.01002959,0.001131809,0.0007027643,0.001613726,0.0001780071],"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":[0.001104436,0.0008109091,0.01746476,0.0005797694,0.0004134251,0.0001433897,0.001062256,0.1986536,0.01655161,0.01660568,0.004392576,0.7422175],"study_design_scores_gemma":[0.00003841557,0.00007182074,0.00200813,0.00001429197,0.00004104706,0.00001960741,0.00006380647,0.9904394,0.002236004,0.00449402,0.0005476074,0.00002587572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1850402,0.0009714494,0.8075661,0.00057708,0.00004306172,0.000260439,0.0003241234,0.002227863,0.002989659],"genre_scores_gemma":[0.7107536,0.0003045104,0.2866108,0.00005746873,0.0001068899,0.0001986705,0.0005156841,0.0001442464,0.001308033],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00649031,"threshold_uncertainty_score":0.02080804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01925216485857263,"score_gpt":0.2726777317676728,"score_spread":0.2534255669091002,"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."}}