{"id":"W2473235395","doi":"10.1145/2930238.2930293","title":"Effect of Different Implicit Social Networks on Recommending Research Papers","year":2016,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bookmarking; Recommender system; Computer science; Collaborative filtering; Audience measurement; Social network (sociolinguistics); World Wide Web; Information retrieval; Social media; Domain (mathematical analysis); Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.019634,0.0009981324,0.00102343,0.005302856,0.001526541,0.003964825,0.001236161,0.00221192,0.0007224776],"category_scores_gemma":[0.1675023,0.0007338442,0.0009524835,0.002833281,0.00123077,0.006283235,0.002148554,0.001386675,0.0003045137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001042555,"about_ca_system_score_gemma":0.0009353626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006624207,"about_ca_topic_score_gemma":0.01055901,"domain_scores_codex":[0.9838754,0.00775173,0.001964969,0.001885296,0.003874403,0.0006481209],"domain_scores_gemma":[0.6653655,0.2984666,0.009830397,0.01146148,0.01323732,0.001638662],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003334731,0.0009370671,0.4940859,0.001345364,0.001548488,0.0008606666,0.003249473,0.1739253,0.01178813,0.005076009,0.0014741,0.3023747],"study_design_scores_gemma":[0.0002423284,0.001236225,0.1597097,0.0002610099,0.001626789,0.001310057,0.0009751381,0.8006026,0.02256788,0.007972747,0.003308177,0.0001873594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9573642,0.001452664,0.03507016,0.000602946,0.00006230732,0.0001545904,0.0004085838,0.0006843999,0.004200106],"genre_scores_gemma":[0.9803323,0.000331973,0.01788415,0.00004847616,0.00006293175,0.00005717855,0.0005083479,0.00005084741,0.0007237286],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.980366,"threshold_uncertainty_score":0.1038357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04026202066835955,"score_gpt":0.3542433904589081,"score_spread":0.3139813697905485,"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."}}