{"id":"W2082692931","doi":"10.1061/(asce)cp.1943-5487.0000436","title":"Data Fusion Process Management for Automated Construction Progress Estimation","year":2014,"lang":"en","type":"article","venue":"Journal of Computing in Civil Engineering","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Sensor fusion; Schedule; Process (computing); Computer science; Field (mathematics); Facility management; Data management; Data mining; Construction management; Systems engineering; Engineering; Artificial intelligence","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":[0.006009498,0.001069646,0.001181307,0.002982827,0.001072313,0.002932947,0.00197391,0.0008700764,0.001301788],"category_scores_gemma":[0.007268521,0.0004949622,0.001233306,0.002332349,0.0007577383,0.003505242,0.002516516,0.001481559,0.0006520784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001463609,"about_ca_system_score_gemma":0.003072528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007646941,"about_ca_topic_score_gemma":0.004403398,"domain_scores_codex":[0.9957842,0.0006917515,0.0004431064,0.0008292236,0.001962686,0.0002889239],"domain_scores_gemma":[0.9962863,0.0009756963,0.0005589647,0.0007014339,0.001332508,0.0001450504],"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":[0.0002821047,0.0002849438,0.005873322,0.0003112066,0.000171692,0.0002448749,0.0009491172,0.2995411,0.01984414,0.04776002,0.004380842,0.6203566],"study_design_scores_gemma":[0.0000193711,0.00006908813,0.001540607,0.00004426104,0.00005377695,0.00007883553,0.0001584381,0.9463424,0.01939504,0.01891636,0.01332794,0.00005384213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002631154,0.0001081356,0.9951466,0.00006386185,0.00001467144,0.00006144897,0.00007986958,0.001377111,0.0005172396],"genre_scores_gemma":[0.2869362,0.0003370107,0.7101294,0.00007612518,0.00005090577,0.000290725,0.001086538,0.0001756186,0.0009174406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007646941,"threshold_uncertainty_score":0.03178167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01712503346196956,"score_gpt":0.2546769847722177,"score_spread":0.2375519513102482,"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."}}