{"id":"W2061113259","doi":"10.1061/(asce)cp.1943-5487.0000101","title":"Reliability-Based Hybrid Data Fusion Method for Adaptive Location Estimation in Construction","year":2011,"lang":"en","type":"article","venue":"Journal of Computing in Civil Engineering","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sensor fusion; Robustness (evolution); Computer science; Data mining; Scalability; Key (lock); Reliability (semiconductor); Machine learning; Database","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002725916,0.0007136073,0.001267396,0.001401923,0.0004840301,0.001092192,0.001314508,0.001011889,0.0007965193],"category_scores_gemma":[0.007807437,0.0004851366,0.0008835159,0.00129533,0.000687667,0.001846559,0.001256699,0.001053113,0.0003413681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008694615,"about_ca_system_score_gemma":0.000844304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003032025,"about_ca_topic_score_gemma":0.002415082,"domain_scores_codex":[0.9982978,0.0005003667,0.0001341152,0.0003522417,0.0006308845,0.00008469202],"domain_scores_gemma":[0.996994,0.001496726,0.0003365983,0.0002620191,0.0008527432,0.00005787407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004183074,0.0001038981,0.002077195,0.0002282126,0.0002097837,0.000165423,0.0003814281,0.5369763,0.01382422,0.01171671,0.00120999,0.4326885],"study_design_scores_gemma":[0.000008140189,0.00004116308,0.0003739858,0.000007338347,0.00001901053,0.00004166951,0.00001965552,0.9936842,0.002635724,0.002656729,0.0004952317,0.00001705049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006140184,0.0001300587,0.9932464,0.00004914132,0.00001597081,0.00001317213,0.00001630946,0.0001786406,0.000210091],"genre_scores_gemma":[0.5882632,0.0003110336,0.4096705,0.00007809125,0.00006246258,0.0001500335,0.000163126,0.00005917584,0.001242383],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003032025,"threshold_uncertainty_score":0.01441616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02067039866723738,"score_gpt":0.254568350408473,"score_spread":0.2338979517412356,"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."}}