{"id":"W2075116457","doi":"10.1080/01446193.2011.637568","title":"A correlated bidding model for markup size decisions","year":2011,"lang":"en","type":"article","venue":"Construction Management and Economics","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Bidding; Markup language; Bayesian probability; A priori and a posteriori; Econometrics; Computer science; Statistical model; Probabilistic logic; Correlation; Data mining; Operations research; Machine learning; Artificial intelligence; Economics; Mathematics; Microeconomics; XML","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":[0.007177034,0.001442932,0.002325518,0.001494839,0.0007639829,0.00271878,0.004906221,0.003217068,0.009653216],"category_scores_gemma":[0.01327351,0.001286428,0.001883219,0.002330704,0.001825362,0.003971647,0.001401381,0.003343035,0.002437294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002358629,"about_ca_system_score_gemma":0.001999125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007816162,"about_ca_topic_score_gemma":0.008021026,"domain_scores_codex":[0.9956533,0.001734509,0.0001765112,0.0008938652,0.0009496562,0.0005921677],"domain_scores_gemma":[0.9925552,0.004438266,0.001308008,0.0004598389,0.000945825,0.0002927515],"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":[0.0001920412,0.0001355797,0.002338989,0.0001049034,0.0001111914,0.0004062649,0.0002750169,0.69213,0.001432894,0.2860211,0.002535327,0.01431668],"study_design_scores_gemma":[0.00002620703,0.00005384369,0.0005779365,0.000008814623,0.00003254961,0.00009667863,0.00002483483,0.9663376,0.0001425241,0.03179721,0.0008644132,0.00003731956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06199146,0.0004069977,0.9199349,0.001316596,0.0000858109,0.0002427374,0.0008572416,0.0002787407,0.01488563],"genre_scores_gemma":[0.8679475,0.0008678236,0.08960167,0.000336591,0.0001869827,0.0006217598,0.0008166069,0.0001237956,0.03949731],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009653216,"threshold_uncertainty_score":0.0379563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05063624431448106,"score_gpt":0.2132746729793665,"score_spread":0.1626384286648854,"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."}}