{"id":"W2005064346","doi":"10.1115/detc2004-57739","title":"Handling Imprecise and Uncertain Engineering Information in IDEF1X and Relational Data Models","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Database design; Relational database; Database model; Conceptual schema; Logical data model; Relational model; Data modeling; Database schema; IDEF1X; Entity–relationship model; Database theory; Fuzzy logic; Database; Information engineering; Data mining; Information system; Software engineering; Artificial intelligence; Engineering; Ontology-based data integration","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":[0.007223319,0.0006313241,0.0009647401,0.002320154,0.001033387,0.006531002,0.002189636,0.001809261,0.001433158],"category_scores_gemma":[0.0145612,0.0005697517,0.001310925,0.00281694,0.002364743,0.01123761,0.003078515,0.002132335,0.0005297693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001433843,"about_ca_system_score_gemma":0.00118811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003840606,"about_ca_topic_score_gemma":0.002520772,"domain_scores_codex":[0.9929396,0.002263919,0.0008099464,0.0008077387,0.002789152,0.0003895975],"domain_scores_gemma":[0.9937504,0.003155198,0.000758304,0.001406788,0.0007847245,0.0001445614],"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":[0.00006931758,0.00003992169,0.001093666,0.0001159771,0.00002564824,0.0007500927,0.001141547,0.04086855,0.001160299,0.9035375,0.001837522,0.04936001],"study_design_scores_gemma":[0.00003096461,0.00008497052,0.0004651782,0.0001849121,0.00005601456,0.001208522,0.0007193451,0.3392535,0.0032605,0.6141774,0.04048539,0.00007332326],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01062196,0.0006708694,0.9835053,0.0007967702,0.00005550496,0.00006160443,0.0001995599,0.0001906018,0.003897828],"genre_scores_gemma":[0.3108452,0.002463931,0.6776266,0.0004856618,0.0002427579,0.0003402149,0.00100598,0.00008302089,0.006906564],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007223319,"threshold_uncertainty_score":0.03820103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03532813237400795,"score_gpt":0.2388644794418641,"score_spread":0.2035363470678561,"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."}}