{"id":"W3121881764","doi":"10.5167/uzh-174509","title":"Perceiving prospects properly","year":2016,"lang":"en","type":"preprint","venue":"Zurich Open Repository and Archive (University of Zurich)","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Status quo; Curse; Action (physics); Perception; Noise (video); Key (lock); Status quo bias; Mathematical economics; Winner's curse; Computer science; Microeconomics; Econometrics; Economics; Psychology; Artificial intelligence; Computer security; Common value auction; Sociology","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":[0.001458229,0.0005562789,0.000360078,0.0006352058,0.0004067999,0.005053394,0.0005240936,0.001410034,0.008674649],"category_scores_gemma":[0.0127143,0.0003180229,0.0005207505,0.0005433013,0.001557515,0.009446689,0.001623498,0.001832745,0.001579071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000374562,"about_ca_system_score_gemma":0.0004828948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007629629,"about_ca_topic_score_gemma":0.0005654144,"domain_scores_codex":[0.9990871,0.0002722784,0.00004856787,0.0002611587,0.0002429293,0.00008790682],"domain_scores_gemma":[0.9983128,0.000559725,0.0002816266,0.0003416141,0.0002974542,0.0002069004],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003875293,0.00007663278,0.009912556,0.0003110887,0.0001319722,0.0005016897,0.005084769,0.0106746,0.01778471,0.8228056,0.009629988,0.1226989],"study_design_scores_gemma":[0.00003920838,0.0001771084,0.01031439,0.0001402653,0.00007227925,0.0004015646,0.00267353,0.02274349,0.003190294,0.9294393,0.03073448,0.0000742128],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2636861,0.001983017,0.4878504,0.009922202,0.001089643,0.0001813695,0.001154069,0.0005638686,0.2335694],"genre_scores_gemma":[0.950065,0.001003314,0.0412615,0.0006192318,0.0001720006,0.00007321465,0.0004733124,0.0001305264,0.006201913],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008674649,"threshold_uncertainty_score":0.02901959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07831609328193978,"score_gpt":0.3173156876215828,"score_spread":0.238999594339643,"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."}}