{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000344599,0.000102678,0.0001151862,0.00004552701,0.0001466209,0.0001699442,0.00160563,0.00003307273,0.000108742],"category_scores_gemma":[0.00005138378,0.00004600428,0.00004945277,0.0002187963,0.0000807886,0.0003925157,0.0003914717,0.00006818877,0.0006597871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001408867,"about_ca_system_score_gemma":0.0001220256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002496864,"about_ca_topic_score_gemma":0.00001822678,"domain_scores_codex":[0.9990312,0.0001066471,0.000136956,0.0002909773,0.0002108586,0.0002233532],"domain_scores_gemma":[0.9986537,0.0001666843,0.00006996978,0.0009702938,0.00007294321,0.00006637991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001046208,0.0000730641,0.003530954,0.00001158999,0.00006937041,0.00002003427,0.001068289,0.000008073921,0.3752605,0.2168818,0.01297989,0.3900859],"study_design_scores_gemma":[0.003564394,0.0005389406,0.01590564,0.001584318,0.0002664982,0.0001727776,0.0006094793,0.3218737,0.1896427,0.053521,0.4094898,0.002830779],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1070013,0.00009651006,0.8310732,0.04069058,0.0004241367,0.0001015229,0.00001572738,0.0003972149,0.02019981],"genre_scores_gemma":[0.9946221,0.0001032422,0.001664474,0.0002604438,0.0000569468,0.000006880768,8.696129e-7,0.000004074708,0.003280965],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8876209,"threshold_uncertainty_score":0.8480448,"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."}}