{"id":"W3137787241","doi":"10.2139/ssrn.3700525","title":"Optimal Auction Design with Deferred Inspection and Reward","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Business; Computer science","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":[0.007114382,0.001696666,0.003593266,0.001209529,0.000616911,0.004028085,0.002979117,0.003551509,0.004777536],"category_scores_gemma":[0.01801362,0.001643067,0.001155111,0.001175039,0.001769553,0.003850404,0.001633276,0.002027436,0.0008867122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0017742,"about_ca_system_score_gemma":0.002451872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001035477,"about_ca_topic_score_gemma":0.0007370218,"domain_scores_codex":[0.9950328,0.002661963,0.0002261268,0.0007034492,0.0007298743,0.0006458208],"domain_scores_gemma":[0.9885734,0.007586065,0.0009993791,0.0009705658,0.001266129,0.0006043969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001479591,0.0005235384,0.0008738557,0.000578135,0.000156998,0.0004044301,0.000159937,0.8000722,0.004972111,0.1355856,0.003429299,0.05176422],"study_design_scores_gemma":[0.0002105753,0.0002786925,0.0001894929,0.00002348717,0.00004460284,0.000138403,0.00002373042,0.9336413,0.0006145713,0.06421103,0.0005916165,0.00003250231],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04617652,0.0003420367,0.9445204,0.0005237046,0.00009052049,0.0002545085,0.000112004,0.0003294459,0.00765079],"genre_scores_gemma":[0.8632962,0.0002848183,0.1264183,0.0001315099,0.0001126814,0.0002427863,0.00008493746,0.00007815461,0.009350548],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007114382,"threshold_uncertainty_score":0.0376249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07125996221903144,"score_gpt":0.3206698294570235,"score_spread":0.2494098672379921,"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."}}