{"id":"W1457225595","doi":"","title":"Validating ontologies in informatics systems: approaches and lessons learned for AEC","year":2014,"lang":"en","type":"article","venue":"Journal of Information Technology in Construction","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Benchmarking; Scope (computer science); Computer science; Construct (python library); Ontology; Dimension (graph theory); Data science; Set (abstract data type); Knowledge management; Informatics; Artificial intelligence; Software engineering; Engineering; Mathematics","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":[0.001070515,0.00008330822,0.0002484749,0.001155935,0.00005542729,0.0001204702,0.0003226479,0.0001925636,1.749332e-7],"category_scores_gemma":[0.001085601,0.00007060626,0.00002523949,0.0003711774,0.0001238143,0.002176748,0.00007539992,0.0002301077,8.212095e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006765837,"about_ca_system_score_gemma":0.00005868486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007862495,"about_ca_topic_score_gemma":0.000009806667,"domain_scores_codex":[0.9987767,0.0000355522,0.0008736743,0.00005108426,0.0001121859,0.0001507906],"domain_scores_gemma":[0.9986933,0.0001999506,0.0008183633,0.0001438592,0.0001280758,0.00001647418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001103348,0.000007797305,0.01275741,0.00008513682,0.000008409428,4.070709e-7,0.001160343,0.001448798,0.00002454393,0.4405581,0.00001827322,0.5439198],"study_design_scores_gemma":[0.004075916,0.0006223611,0.01065573,0.000494783,0.00002040737,0.002274895,0.06704501,0.6690238,0.00548619,0.2375296,0.002360057,0.0004112674],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2372993,0.00007174412,0.7588316,0.002943969,0.0003431633,0.000141075,5.402345e-7,0.00005771998,0.0003108616],"genre_scores_gemma":[0.8079144,0.00005430218,0.1919846,0.00002535914,0.00001100221,0.000007444851,6.4177e-7,0.000001275817,9.046007e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.667575,"threshold_uncertainty_score":0.287924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04730560788840749,"score_gpt":0.2756129068634334,"score_spread":0.2283072989750259,"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."}}