{"id":"W1684552989","doi":"10.1007/978-3-642-41924-9_4","title":"TBIM: A Language for Modeling and Reasoning about Business Plans","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Business rule; Business Process Model and Notation; Business process modeling; Business intelligence; Artifact-centric business process model; Syntax; Knowledge management; Strategic planning; Process management; Modeling language; Software engineering; Business process; Management science; Artificial intelligence; Programming language; Management; Engineering; Work in process; Operations management","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":[0.002803753,0.002174015,0.001322381,0.003571759,0.001485606,0.006693026,0.005322384,0.002128885,0.01956819],"category_scores_gemma":[0.007051675,0.002550031,0.004194095,0.003910949,0.00191865,0.0102017,0.003916391,0.005219933,0.008011285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002319324,"about_ca_system_score_gemma":0.003798516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01720523,"about_ca_topic_score_gemma":0.01930796,"domain_scores_codex":[0.9979957,0.0005034153,0.0004756185,0.0003001006,0.0005749846,0.0001501887],"domain_scores_gemma":[0.9978724,0.001007148,0.0001996225,0.0004940752,0.0003160329,0.0001107762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001754689,0.0001235157,0.001079436,0.001533422,0.0001341489,0.0005252856,0.001503365,0.03840666,0.002791299,0.7069358,0.0913057,0.1554859],"study_design_scores_gemma":[0.00009410734,0.00004247381,0.0001946544,0.0004481801,0.0001153518,0.000363343,0.0003133504,0.1963623,0.005225686,0.4258077,0.3709436,0.00008930712],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008077511,0.0002506028,0.9748368,0.0003847051,0.00008778714,0.0002165172,0.006243653,0.01319672,0.003975374],"genre_scores_gemma":[0.02256361,0.0006644359,0.9538183,0.0004034853,0.00007024259,0.0007649146,0.0138698,0.00262233,0.005222855],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01956819,"threshold_uncertainty_score":0.06546205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03711594633469067,"score_gpt":0.2641250558203415,"score_spread":0.2270091094856508,"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."}}