{"id":"W7114802657","doi":"10.1016/j.aei.2025.104109","title":"WAM-ONTO: A semantic framework for water infrastructure asset management","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Water Systems and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Asset management; Ontology; Workflow; SPARQL; Asset (computer security); IT asset management; Semantic Web; Consistency (knowledge bases); XBRL; Domain (mathematical analysis)","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":[0.00006570081,0.0002015559,0.0001969878,0.0001583599,0.00004668993,0.00006672942,0.0001505104,0.0001115993,0.000005682379],"category_scores_gemma":[0.00001070402,0.0001700126,0.0000542528,0.0001587008,0.000005401581,0.0003123453,0.00003980671,0.0001341142,0.00001165783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008720981,"about_ca_system_score_gemma":0.000003246547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.380355e-7,"about_ca_topic_score_gemma":9.387576e-7,"domain_scores_codex":[0.9991014,0.000001677791,0.0004014107,0.00007050929,0.00009393518,0.0003310684],"domain_scores_gemma":[0.9996161,0.00002708482,0.00002226666,0.0002568251,0.00003633212,0.00004138249],"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":[0.000002706377,0.000002319659,0.00001993273,0.001743269,0.00007911948,4.906485e-7,0.0004486448,0.9843128,0.0000715774,0.009900069,0.001255276,0.002163765],"study_design_scores_gemma":[0.0003873771,0.000012387,0.000172254,0.0003535995,0.00003193353,0.000002396198,0.0001148873,0.9014248,0.003674113,0.001295043,0.09228271,0.0002485387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008564807,0.00006915127,0.9869834,0.00001843071,0.001264779,0.0004476746,0.0000101789,0.0005166645,0.00212494],"genre_scores_gemma":[0.4725677,0.00007042543,0.5265062,0.00006760464,0.00005759352,0.0001721414,0.00008308583,0.00004442307,0.0004308161],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4640029,"threshold_uncertainty_score":0.6932911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002006817031483429,"score_gpt":0.1936836122865923,"score_spread":0.1916767952551089,"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."}}