{"id":"W1995811343","doi":"10.1016/j.autcon.2010.11.009","title":"Formalisms for query capture and data source identification to support data fusion for construction productivity monitoring","year":2010,"lang":"en","type":"article","venue":"Automation in Construction","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"National Science Foundation","keywords":"Computer science; Identification (biology); Rotation formalisms in three dimensions; Data mining; Sensor fusion; Set (abstract data type); Data set; Data source; Data integration; Machine learning; 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":[0.02259341,0.0009880267,0.001711118,0.005957386,0.002363349,0.01024182,0.004210747,0.002641101,0.002984049],"category_scores_gemma":[0.04043877,0.00178317,0.004171536,0.005296483,0.003747181,0.01254134,0.006874298,0.005035614,0.001107466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002761706,"about_ca_system_score_gemma":0.005787993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01176466,"about_ca_topic_score_gemma":0.01555363,"domain_scores_codex":[0.987667,0.003838545,0.002634613,0.001442501,0.003801924,0.000615456],"domain_scores_gemma":[0.9743496,0.01074013,0.001622846,0.009212168,0.003600709,0.000474565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001917357,0.0002845143,0.002507738,0.0004713822,0.0002772056,0.0007197384,0.002832338,0.02981726,0.007011126,0.8384737,0.008969921,0.1084433],"study_design_scores_gemma":[0.00009027157,0.0000477982,0.0007856117,0.000376542,0.0002968737,0.000440256,0.0006776382,0.3164474,0.01556176,0.6217259,0.043397,0.0001529172],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00187981,0.00009042646,0.9945623,0.0003949296,0.00002865694,0.0001823587,0.0003459591,0.001502487,0.001013005],"genre_scores_gemma":[0.08500641,0.0002957886,0.911052,0.0003413461,0.00005746403,0.0004534472,0.001467593,0.0004305029,0.0008953658],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02259341,"threshold_uncertainty_score":0.1194868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05345153562988367,"score_gpt":0.329836415295886,"score_spread":0.2763848796660023,"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."}}