{"id":"W3139529231","doi":"10.5555/2872518.3251210","title":"Session details: PhD Symposium","year":2016,"lang":"en","type":"article","venue":"The Web Conference","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Session (web analytics); Gratitude; Library science; Excellence; Variety (cybernetics); Pleasure; Computer science; Political science; World Wide Web; Operations research; Psychology; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00390583,0.001574451,0.00129703,0.001859955,0.003367667,0.009949576,0.002471925,0.006969021,0.5770003],"category_scores_gemma":[0.008936261,0.0005893099,0.001484264,0.001313496,0.0006686338,0.004026322,0.005278617,0.006087283,0.4585137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002927081,"about_ca_system_score_gemma":0.007355502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001128935,"about_ca_topic_score_gemma":0.001981577,"domain_scores_codex":[0.9972177,0.0004197145,0.0002019336,0.0006100023,0.00104526,0.0005053115],"domain_scores_gemma":[0.9901097,0.0004185957,0.0002909009,0.0004332071,0.003506713,0.00524079],"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":[0.00004960227,0.00005120484,0.00009610078,0.0001493224,0.000004174427,0.00004277403,0.00003108723,0.00002396423,0.000166271,0.001109954,0.9772678,0.02100771],"study_design_scores_gemma":[0.00001796184,0.00003906869,0.0003260424,0.0001199116,0.000003904264,0.0000589332,0.00006098356,0.00003028184,0.00008653348,0.0007928409,0.9984566,0.000007016565],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.001591765,0.0123341,0.002618419,0.1002092,0.3967288,0.001114276,0.006815241,0.002039656,0.4765485],"genre_scores_gemma":[0.007183834,0.006671427,0.001517083,0.01860474,0.07070868,0.0007850752,0.003273625,0.0009011814,0.8903543],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.4229997,"threshold_uncertainty_score":0.6033577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0365446678869835,"score_gpt":0.2468162468180241,"score_spread":0.2102715789310406,"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."}}