{"id":"W4293069251","doi":"10.1007/978-981-19-1029-6_32","title":"Using Data Mining for Prioritizing Roof Rehabilitation Works","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cluster analysis; Task (project management); Process (computing); Computer science; Roof; Visual inspection; Asset (computer security); Criticality; Rehabilitation; Data mining; Engineering; Civil engineering; Artificial intelligence; Systems engineering; Medicine; Computer security","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":[0.0009604471,0.001590063,0.001314483,0.006101679,0.0006146043,0.002559552,0.001554473,0.001038627,0.002577136],"category_scores_gemma":[0.002571422,0.0004881095,0.001620809,0.004667036,0.0002422363,0.001623021,0.001037443,0.0007398221,0.00203291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005163429,"about_ca_system_score_gemma":0.0009291357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009076006,"about_ca_topic_score_gemma":0.01481621,"domain_scores_codex":[0.9989015,0.0001382994,0.0001605795,0.0003302546,0.0003592639,0.0001100371],"domain_scores_gemma":[0.9982708,0.0009480092,0.0001566112,0.0001646422,0.0003722803,0.00008772683],"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":[0.0004700564,0.0009137121,0.05191302,0.0006746894,0.0004509792,0.000456613,0.0002056711,0.0649832,0.01015622,0.002604929,0.0136908,0.8534802],"study_design_scores_gemma":[0.00007675016,0.0004295817,0.02963176,0.0002940928,0.0004128321,0.0005516701,0.000991652,0.9077251,0.0221225,0.01677899,0.02089526,0.00008986224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2500628,0.004493251,0.6890417,0.001350998,0.0005075163,0.0009959029,0.02482827,0.008905443,0.01981412],"genre_scores_gemma":[0.5302188,0.001516955,0.4364724,0.0002187961,0.0001740016,0.0003867968,0.02487229,0.0002399557,0.005899939],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009076006,"threshold_uncertainty_score":0.01804632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02550924267619925,"score_gpt":0.249809859856522,"score_spread":0.2243006171803227,"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."}}