{"id":"W2154768509","doi":"10.14778/1453856.1453934","title":"Efficient network aware search in collaborative tagging sites","year":2008,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":112,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Popularity; Cluster analysis; Seekers; Context (archaeology); Upper and lower bounds; Heuristic; Space (punctuation); Information retrieval; Data mining; Machine learning; Artificial intelligence; Mathematics; Geography","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":[0.003343349,0.0008142266,0.002247597,0.003186424,0.001752146,0.002921827,0.002772148,0.001806523,0.001515398],"category_scores_gemma":[0.01742416,0.0007578142,0.000850577,0.005506529,0.00119131,0.005478757,0.002698744,0.000906485,0.0009563499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001555894,"about_ca_system_score_gemma":0.001614328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005331591,"about_ca_topic_score_gemma":0.009775597,"domain_scores_codex":[0.9971719,0.0009333146,0.0002133403,0.0006724093,0.0006092726,0.0003997342],"domain_scores_gemma":[0.989407,0.00634803,0.001103732,0.001869678,0.0008283364,0.0004432292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001702193,0.0006145177,0.02922441,0.0007888292,0.0003021344,0.0005879742,0.002218715,0.587754,0.02310791,0.04567719,0.01101087,0.2970113],"study_design_scores_gemma":[0.00005298417,0.0001070943,0.001653003,0.00001774126,0.00005585298,0.0002309767,0.0003338365,0.9607445,0.004027523,0.03103467,0.001715783,0.00002599858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3467378,0.001554494,0.6432123,0.0007131455,0.00004540091,0.0002427851,0.0008934593,0.001873292,0.004727425],"genre_scores_gemma":[0.7774791,0.0003591123,0.2172243,0.0001038446,0.00006549033,0.0001233702,0.00128899,0.0001815646,0.003174167],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005331591,"threshold_uncertainty_score":0.01768148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01881665465521546,"score_gpt":0.234770653360951,"score_spread":0.2159539987057356,"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."}}