{"id":"W2095743800","doi":"10.1109/icdm.2005.120","title":"Predicting Software Escalations with Maximum ROI","year":2006,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Vendor; Return on investment; Reputation; Revenue; Software; Computer science; Investment (military); Product (mathematics); Business; Marketing; Finance; Operating system","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":[0.0001116216,0.00007856235,0.00006311364,0.00009443411,0.00009417141,0.0001389631,0.0004255124,0.0000296637,0.00002694539],"category_scores_gemma":[0.0001277769,0.00006257129,0.00001922858,0.0004678212,0.00002010685,0.0003231408,0.0001267256,0.0001117748,0.00006127712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003601002,"about_ca_system_score_gemma":0.00004747186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001936542,"about_ca_topic_score_gemma":0.00004020607,"domain_scores_codex":[0.9990808,0.00001069511,0.00009785756,0.0002301142,0.0003182543,0.0002622568],"domain_scores_gemma":[0.9991179,0.0003462172,0.00001605221,0.0003870941,0.00007601082,0.00005673322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000001662776,0.00004463435,0.9632291,0.00001297245,0.00000986118,0.0000267875,0.00006848056,0.01507997,0.00006415466,0.01042095,0.002335969,0.008705523],"study_design_scores_gemma":[0.0003720688,0.0000930883,0.890209,0.00002904375,0.000003250292,0.00005646676,0.000008845481,0.102389,0.001282973,0.003450637,0.001834039,0.0002716024],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.057871,0.0000217762,0.9385918,0.0002622667,0.0000642984,0.00008517382,7.676504e-7,0.001244325,0.001858629],"genre_scores_gemma":[0.6802802,2.465419e-7,0.3184227,0.00002077701,0.00006052375,0.00001409103,0.000002123837,0.000009816177,0.001189513],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6224092,"threshold_uncertainty_score":0.2551583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008598635395594132,"score_gpt":0.2201097054914591,"score_spread":0.211511070095865,"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."}}