{"id":"W2560290351","doi":"10.1109/mtd.2016.14","title":"Adjusting the Balance Sheet by Appending Technical Debt","year":2016,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Technical debt; Debt; Obligation; Balance sheet; Liability; Recourse debt; Off-balance-sheet; Scope (computer science); Business; Internal debt; Debt levels and flows; Finance; Computer science; Software; Law; Political science; Software development","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00441358,0.0008757422,0.0006303579,0.002590642,0.001331096,0.005729442,0.001358992,0.002113188,0.01168417],"category_scores_gemma":[0.02989921,0.0004685609,0.000676535,0.002847955,0.001318348,0.007996229,0.003075471,0.001977836,0.003724834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001320027,"about_ca_system_score_gemma":0.001454274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001201311,"about_ca_topic_score_gemma":0.0008135057,"domain_scores_codex":[0.9969053,0.000687715,0.0004964683,0.0004125894,0.001242581,0.0002553291],"domain_scores_gemma":[0.9812384,0.005221037,0.005813081,0.003334821,0.003518021,0.0008746139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008463801,0.0005896664,0.06116324,0.00127532,0.0002563035,0.003715,0.002772223,0.03563943,0.02211324,0.2473955,0.02210642,0.6021273],"study_design_scores_gemma":[0.0003089814,0.001116015,0.08766102,0.002440421,0.0005223894,0.004698937,0.003277877,0.06228269,0.03159087,0.3772166,0.4283804,0.0005037991],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.589077,0.01103081,0.1962493,0.008440983,0.00188883,0.0004889286,0.001501807,0.002627957,0.1886944],"genre_scores_gemma":[0.8984661,0.003952048,0.05240599,0.0009443914,0.0005974247,0.0001995563,0.001254278,0.0005099694,0.04167017],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01168417,"threshold_uncertainty_score":0.03908747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01328763928775241,"score_gpt":0.2560546116292279,"score_spread":0.2427669723414755,"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."}}