{"id":"W1975392223","doi":"10.1061/(asce)co.1943-7862.0000364","title":"Improving Construction Supply Network Visibility by Using Automated Materials Locating and Tracking Technology","year":2011,"lang":"en","type":"article","venue":"Journal of Construction Engineering and Management","topic":"BIM and Construction Integration","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visibility; Supply network; Dependency (UML); Work (physics); Computer science; Tracking (education); Supply chain; Field (mathematics); Risk analysis (engineering); Systems engineering; Business; Engineering; Artificial intelligence; Marketing","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002573527,0.0001662164,0.0002339465,0.0002897578,0.00008812802,0.00006740406,0.00005533802,0.0001109095,0.00001752242],"category_scores_gemma":[0.0000136136,0.0001695496,0.00002822086,0.0002243686,0.00009390858,0.0003452783,0.00002548683,0.0001675932,3.081286e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006714213,"about_ca_system_score_gemma":0.000008601384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008784401,"about_ca_topic_score_gemma":6.946553e-7,"domain_scores_codex":[0.9990337,0.00001618278,0.0005219809,0.0001336562,0.0001041636,0.0001903346],"domain_scores_gemma":[0.9995511,0.00001147585,0.0001952742,0.00009008681,0.00008928617,0.0000627692],"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":[0.0000734638,0.00003062806,0.008263956,0.001237816,0.0006910736,0.00003164023,0.0004020206,0.03822875,0.1669617,0.02656725,0.0002388386,0.7572729],"study_design_scores_gemma":[0.003499197,0.0003582875,0.01024451,0.001728993,0.0008629093,0.0132862,0.005371437,0.8281143,0.129259,0.003495417,0.002183174,0.001596588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.815094,0.0007680196,0.1819932,0.0000129198,0.001525043,0.0001263393,0.000003427657,0.000361148,0.0001159124],"genre_scores_gemma":[0.9069264,0.0003236306,0.0926236,0.000004299558,0.00009586804,0.000003711589,0.000001149858,0.00001916477,0.000002206819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7898855,"threshold_uncertainty_score":0.6914032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006045387046516512,"score_gpt":0.1855300363718357,"score_spread":0.1794846493253192,"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."}}