{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001204355,0.0001290321,0.0001583629,0.0002177049,0.0002415035,0.0002733756,0.00079368,0.000132296,0.000001759595],"category_scores_gemma":[0.000805449,0.0001334767,0.00001486548,0.0002593206,0.0000899506,0.003087547,0.0004422975,0.000121479,0.000002321685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003071819,"about_ca_system_score_gemma":0.00009378353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008631579,"about_ca_topic_score_gemma":0.0002129322,"domain_scores_codex":[0.9984249,0.00003584399,0.000390641,0.0007636906,0.0001790224,0.000205902],"domain_scores_gemma":[0.9979291,0.000173566,0.0002368155,0.001436579,0.0001725486,0.00005140361],"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":[0.00002629787,0.00002573239,0.01263639,0.0001100145,0.000009830623,2.623709e-7,0.0006614649,0.00005057135,0.04946727,0.01355024,0.0006192336,0.9228427],"study_design_scores_gemma":[0.002365601,0.0002269938,0.174606,0.0001275869,0.00009209415,0.0007409024,0.002624448,0.6727471,0.09843561,0.02250177,0.02453527,0.000996583],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3692995,0.000009173708,0.6258846,0.00103462,0.002780586,0.0007462278,0.0000624616,0.000171374,0.00001144702],"genre_scores_gemma":[0.6228862,0.000005119552,0.3764392,0.00001786076,0.0002407182,0.00008662292,0.0002986787,0.000007425229,0.00001822071],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9218461,"threshold_uncertainty_score":0.5443022,"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."}}