{"id":"W2887408259","doi":"","title":"Toward Session Consistency for the Edge","year":2018,"lang":"en","type":"article","venue":"","topic":"Team Dynamics and Performance","field":"Psychology","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Session (web analytics); Consistency (knowledge bases); Computer science; Enhanced Data Rates for GSM Evolution; Artificial intelligence; World Wide Web","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001221531,0.0000453953,0.00004798865,0.00001206715,0.0001297132,0.00001116461,0.0001152253,0.00004067472,0.00260312],"category_scores_gemma":[0.000008700804,0.00002428648,0.00003409663,0.00004053722,0.0000787104,0.00002001121,0.0000189459,0.00003968745,0.0004899229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004890796,"about_ca_system_score_gemma":0.0000118679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005619318,"about_ca_topic_score_gemma":0.0000392398,"domain_scores_codex":[0.9996507,0.00000885515,0.00007804014,0.00009930128,0.00003356854,0.0001295124],"domain_scores_gemma":[0.9996084,0.00009578629,0.00002238822,0.0002095073,0.00004416524,0.00001973318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001476606,0.00008595933,0.007803116,0.00001562273,0.00006510512,0.000001059026,0.004456511,4.487585e-7,0.0002275125,0.4497172,0.3275502,0.2099296],"study_design_scores_gemma":[0.0006006434,0.0002825962,0.04066966,0.000007253272,0.00001815225,0.00001395177,0.001043498,0.004635716,0.0001143791,0.002416998,0.9500727,0.0001244288],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2032828,0.0003846008,0.0554939,0.007350462,0.006353891,0.0005562731,0.00002518026,0.0001198946,0.726433],"genre_scores_gemma":[0.9728821,0.000008034226,0.0005351523,0.001170263,0.0003875612,0.00003407864,0.000002347531,0.000006601021,0.02497389],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7695993,"threshold_uncertainty_score":0.9983087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06267783388368411,"score_gpt":0.3568988631297066,"score_spread":0.2942210292460224,"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."}}