{"id":"W2939517799","doi":"","title":"Tool-support of socio-technical coordination in the context of heterogeneous modeling : A research statement and associated roadmap","year":2018,"lang":"en","type":"preprint","venue":"Espace ÉTS (ETS)","topic":"Model-Driven Software Engineering Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Consistency (knowledge bases); Context (archaeology); Set (abstract data type); Statement (logic); Coherence (philosophical gambling strategy); Systems engineering; Model-driven architecture; Unified Modeling Language; Systems modeling; Software engineering; Process management; Management science; Engineering management; Data science; Engineering; Artificial intelligence; Software","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":[0.005370743,0.0002528575,0.0005038963,0.0003905341,0.00006688337,0.00009223808,0.001464757,0.0003822333,0.000005690167],"category_scores_gemma":[0.0001826542,0.0002229114,0.0001073372,0.0003645778,0.000168663,0.0001100246,0.001908728,0.0008246198,0.000002047611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000340777,"about_ca_system_score_gemma":0.0002589489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002773747,"about_ca_topic_score_gemma":0.0001159838,"domain_scores_codex":[0.9967242,0.0005206125,0.0007100637,0.0006206903,0.001025086,0.0003993144],"domain_scores_gemma":[0.997622,0.0002636837,0.0003381553,0.001075394,0.0006478856,0.00005289237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005115767,0.004627468,0.01275233,0.003972394,0.001266017,0.0004094569,0.1592024,0.3848289,0.01607943,0.2262948,0.05373287,0.1363223],"study_design_scores_gemma":[0.0005265721,0.0005324945,0.0007077877,0.0005811251,0.00002123486,0.00001274436,0.0001000347,0.9840024,0.003320578,0.008363903,0.001388403,0.0004427176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2928073,0.0001005118,0.7050388,0.001111191,0.00007586098,0.0006790292,0.00002019509,0.0001378255,0.00002926917],"genre_scores_gemma":[0.9431137,0.00005632075,0.05653002,0.00003835867,0.00002243499,0.0001544886,0.00001656238,0.00002359777,0.00004447144],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6503065,"threshold_uncertainty_score":0.9090064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05326786640818496,"score_gpt":0.3376555024061161,"score_spread":0.2843876359979311,"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."}}