{"id":"W3201158918","doi":"10.22215/etd/2020-13991","title":"Operational performance benchmarking for commercial buildings by using text analytics on work order logs and tenant survey data","year":2020,"lang":"en","type":"dissertation","venue":"","topic":"Facilities and Workplace Management","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Benchmarking; Computer science; Analytics; Work order; Data science; Workflow; Lexicon; Work (physics); Association rule learning; Order (exchange); Data mining; Bayesian network; Engineering; Database; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005627347,0.0003156326,0.0003700921,0.00009660877,0.0003068761,0.0001691508,0.0004015732,0.0002257618,0.000860222],"category_scores_gemma":[0.00007694644,0.0002479618,0.00003807246,0.0002646614,0.00003691393,0.0001013935,0.0001314635,0.0002754945,0.00002761357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005298885,"about_ca_system_score_gemma":0.00005989245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006668153,"about_ca_topic_score_gemma":0.0008070877,"domain_scores_codex":[0.998214,0.00007283053,0.0003991447,0.0007394716,0.0002361126,0.0003384119],"domain_scores_gemma":[0.9989116,0.000285104,0.0001511847,0.0004531571,0.0001231687,0.00007581827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002609134,0.0001998057,0.03062559,0.0003784403,0.0006388172,0.000003610189,0.0017616,0.000469526,0.000008065893,0.001732036,0.8918119,0.06976147],"study_design_scores_gemma":[0.003203709,0.001104361,0.1330143,0.0006747004,0.0005532653,0.000003270163,0.006081935,0.08041852,0.00002593444,0.00002587053,0.7728042,0.00208994],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9239173,0.001090774,0.01231494,0.004155417,0.008709025,0.004404578,0.005455638,0.0001751291,0.03977721],"genre_scores_gemma":[0.6065282,0.0004446752,0.01235842,0.006133595,0.001330125,0.0001793646,0.1870699,0.0002570234,0.1856987],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3173891,"threshold_uncertainty_score":0.9999973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.10135897168125,"score_gpt":0.3511297218875943,"score_spread":0.2497707502063443,"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."}}