{"id":"W2098398624","doi":"10.48550/arxiv.0705.1013","title":"Tracking User Attention in Collaborative Tagging Communities","year":2007,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Metadata; Navigability; Computer science; Usability; World Wide Web; Scalability; Popularity; Information retrieval; Set (abstract data type); Population; Discoverability; Similarity (geometry); Data science; Human–computer interaction; Geography; Database; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001162684,0.0002908485,0.0004173367,0.0004391954,0.000169332,0.0003123259,0.001288228,0.0002975241,0.000008286214],"category_scores_gemma":[0.00001921197,0.0002953744,0.0001040896,0.000459833,0.00007364574,0.0005332338,0.001387814,0.0009196582,0.00001909964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002016133,"about_ca_system_score_gemma":0.0001053492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001585863,"about_ca_topic_score_gemma":0.001095069,"domain_scores_codex":[0.9981087,0.0003122865,0.0005593123,0.0004083364,0.000251152,0.0003601867],"domain_scores_gemma":[0.9982366,0.0001444733,0.0003465259,0.00100788,0.0002086183,0.00005591955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000005906712,0.0001335628,0.9651954,0.0002068244,0.00006716629,0.00006885586,0.01284698,0.00008904489,0.0002955897,0.009030703,0.001522078,0.01053794],"study_design_scores_gemma":[0.0007706722,0.0001303703,0.9324005,0.003502532,0.0000240262,0.00002893607,0.007064615,0.00624238,0.006242491,0.005332915,0.0366399,0.001620664],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7758682,0.0005075095,0.2160851,0.0008416925,0.001270164,0.0005434537,0.000005670579,0.0004725277,0.004405713],"genre_scores_gemma":[0.9888255,0.0001586809,0.01020051,0.0003515476,0.0001142747,0.00008965132,0.00002146576,0.0000233658,0.0002149924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2129574,"threshold_uncertainty_score":0.9999498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07902088863717285,"score_gpt":0.3180221338623009,"score_spread":0.239001245225128,"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."}}