{"id":"W2333800443","doi":"10.1061/9780784413029.084","title":"Applying Regression Analysis to Predict and Classify Construction Cycle Time","year":2013,"lang":"en","type":"article","venue":"Computing in Civil Engineering","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Bridge (graph theory); Precast concrete; Regression analysis; Linear regression; Computer science; Regression; Mean squared error; Square (algebra); Polynomial regression; Engineering; Data mining; Machine learning; Statistics; Mathematics; Structural engineering","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":[0.0008683834,0.0009437042,0.0005551687,0.002728359,0.0002012356,0.0006512294,0.0004268267,0.0006026587,0.0009721965],"category_scores_gemma":[0.004001678,0.0002061275,0.0006118471,0.00231891,0.0001905906,0.0005860544,0.0002692585,0.0006597724,0.0007821521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003665467,"about_ca_system_score_gemma":0.0004743849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0064293,"about_ca_topic_score_gemma":0.003945536,"domain_scores_codex":[0.9994712,0.0001356891,0.00004223503,0.0001424919,0.0001452376,0.00006311802],"domain_scores_gemma":[0.998544,0.000853288,0.0002173116,0.0001157827,0.0002415756,0.00002811246],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001393157,0.0001587492,0.03151143,0.0001193206,0.0001134191,0.0001132071,0.0000887411,0.5731787,0.01091339,0.001844358,0.001460025,0.3803593],"study_design_scores_gemma":[0.000002566279,0.00003653944,0.005598553,0.00000633517,0.00001040022,0.00002590599,0.00002613464,0.9901015,0.002623741,0.001024149,0.0005346876,0.000009502611],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2754607,0.0006340625,0.7177385,0.0002621487,0.00007730191,0.00008529452,0.0008158464,0.002084909,0.002841374],"genre_scores_gemma":[0.8785194,0.0004073925,0.1181548,0.00002908915,0.00004679585,0.00006743168,0.001086388,0.00007965308,0.00160901],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0064293,"threshold_uncertainty_score":0.01278377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006910832997449683,"score_gpt":0.233532636204805,"score_spread":0.2266218032073553,"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."}}