{"id":"W1994071237","doi":"10.1145/2857659.2857660","title":"27 <sup>th</sup> ACM International Conference on Hypertext and Social Media","year":2016,"lang":"en","type":"article","venue":"ACM SIGWEB Newsletter","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Hypertext; Computer science; Personalization; Social media; World Wide Web; Adaptation (eye); Psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002161027,0.0001948513,0.0002112899,0.0001147075,0.00009355899,0.0002217166,0.002006267,0.000122156,0.0002360429],"category_scores_gemma":[0.0001686418,0.0001295514,0.00006606135,0.00007322512,0.00007797098,0.0004846604,0.0009412932,0.0001432564,0.0002521903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004509313,"about_ca_system_score_gemma":0.00002847371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001743294,"about_ca_topic_score_gemma":0.000007463665,"domain_scores_codex":[0.9985468,0.00008762306,0.0002541456,0.0004865323,0.0003370016,0.0002878788],"domain_scores_gemma":[0.9985165,0.0003579872,0.0000940314,0.0008820951,0.00007443273,0.00007493578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001347282,0.00005770853,0.001775801,0.000009447952,0.00005545196,0.00003669729,0.002809423,1.11794e-7,0.006693505,0.06296962,0.6712359,0.2543428],"study_design_scores_gemma":[0.002358058,0.0002477494,0.007714201,0.0002265112,0.00001900945,0.0001343594,0.0002041319,0.001589218,0.00767313,0.1055001,0.8731454,0.001188117],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1157706,0.0000739935,0.08804469,0.7734206,0.002606472,0.0004711976,0.00003339392,0.0007858695,0.01879317],"genre_scores_gemma":[0.9763317,0.00002985009,0.01054057,0.01154097,0.000750389,0.0000311253,0.000003450523,0.0000171622,0.0007548396],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.860561,"threshold_uncertainty_score":0.5282952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05820448069823664,"score_gpt":0.2715417705104575,"score_spread":0.2133372898122209,"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."}}