{"id":"W2758669266","doi":"10.1109/re.2017.62","title":"ECrits — Visualizing Support Ticket Escalation Risk","year":2017,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Ticket; Process (computing); Task (project management); Customer intelligence; Decision support system; IBM; Product (mathematics)","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":[],"consensus_categories":[],"category_scores_codex":[0.0004687281,0.00006754389,0.00007004802,0.00006641733,0.0003512856,0.0005236116,0.001053752,0.00003720954,0.00006472451],"category_scores_gemma":[0.001267934,0.00006201531,0.00002926055,0.00006194908,0.00002505622,0.000701935,0.0004004406,0.000114178,0.0004285669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002701415,"about_ca_system_score_gemma":0.00003776046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009488033,"about_ca_topic_score_gemma":0.000007194387,"domain_scores_codex":[0.9991317,0.00002042913,0.00009771182,0.0002280818,0.0002879578,0.000234124],"domain_scores_gemma":[0.9986466,0.000214012,0.00004546228,0.0009397324,0.0000637321,0.00009049154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000003666012,0.00008504232,0.6213014,0.00003902386,0.00004078688,0.00007566539,0.0014556,0.0002878461,0.001343041,0.04135849,0.01368917,0.3203202],"study_design_scores_gemma":[0.0002584737,0.00006574232,0.8482664,0.00001229677,0.000003073008,0.000009673689,0.000005317927,0.1343481,0.00722099,0.001600666,0.008000731,0.0002085303],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1278546,0.00000878455,0.8661991,0.0003939251,0.0003814508,0.0000809045,7.47377e-7,0.0004238886,0.004656517],"genre_scores_gemma":[0.9624683,0.000007189992,0.03597596,0.00002747332,0.00006453847,0.000005253586,8.314547e-7,0.000007384978,0.001443028],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8346137,"threshold_uncertainty_score":0.5508503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03021268026713663,"score_gpt":0.3306504797441246,"score_spread":0.300437799476988,"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."}}