{"id":"W3167067688","doi":"10.1007/s10270-021-00884-z","title":"Automated generation of consistent, diverse and structurally realistic graph models","year":2021,"lang":"en","type":"article","venue":"Software & Systems Modeling","topic":"Model-Driven Software Engineering Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Research, Development and Innovation Office; Nemzeti Kutatási, Fejlesztési és Innovaciós Alap; Fonds de recherche du Québec – Nature et technologies; Innovációs és Technológiai Minisztérium; Nemzeti Kutatási Fejlesztési és Innovációs Hivatal; Natural Sciences and Engineering Research Council of Canada; Budapesti Műszaki és Gazdaságtudományi Egyetem; McGill University","keywords":"Computer science; Heuristics; Graph; Metamodeling; Theoretical computer science; Set (abstract data type); Consistency (knowledge bases); Data mining; Artificial intelligence; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001247071,0.0009271892,0.0004556183,0.001245876,0.0004310115,0.0007577939,0.001241623,0.001046917,0.00204962],"category_scores_gemma":[0.007232257,0.0006229322,0.00114051,0.00062167,0.0006729593,0.001310118,0.001272144,0.0007820711,0.0004505255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006532765,"about_ca_system_score_gemma":0.0009633652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001680638,"about_ca_topic_score_gemma":0.0051901,"domain_scores_codex":[0.9989819,0.0004054768,0.00004348432,0.0001977616,0.000327016,0.00004433512],"domain_scores_gemma":[0.9955499,0.002941683,0.0002457326,0.0007471851,0.0004283487,0.00008718322],"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":[0.00006455395,0.0001236317,0.002811212,0.0002703414,0.00006526932,0.0005779401,0.0003885357,0.8699021,0.01623288,0.02206496,0.003228073,0.08427045],"study_design_scores_gemma":[0.0000144742,0.0000394985,0.0001613125,0.00001909411,0.00001385074,0.0001116314,0.00008167166,0.9801312,0.005500874,0.01078334,0.003134272,0.000008804102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04876454,0.00007683526,0.9461795,0.0001963641,0.00002379601,0.0002022865,0.0003845825,0.001950189,0.002221926],"genre_scores_gemma":[0.3115803,0.0001079231,0.6849402,0.00008380959,0.000007975608,0.0002478006,0.001486015,0.0005601671,0.0009858303],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00204962,"threshold_uncertainty_score":0.00685668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05521648390240973,"score_gpt":0.2534750130895944,"score_spread":0.1982585291871847,"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."}}