{"id":"W2996300138","doi":"10.2139/ssrn.3360622","title":"Pricing for Heterogeneous Products: Analytics for Ticket Reselling","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Ticket; Analytics; Business; Computer science; Computer security; Data 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002408285,0.0001940448,0.0002501062,0.0002678757,0.0003216627,0.0003047623,0.000297959,0.00006413461,0.00003880229],"category_scores_gemma":[0.000263204,0.0001782299,0.0001818941,0.0003088447,0.00001576877,0.0005069271,0.00004903521,0.000564517,0.00003841081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002227819,"about_ca_system_score_gemma":0.000378489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003587342,"about_ca_topic_score_gemma":0.0001653062,"domain_scores_codex":[0.9972433,0.000009885305,0.0003606746,0.0003047049,0.0002067092,0.001874765],"domain_scores_gemma":[0.9990373,0.000151936,0.0002842303,0.0002201973,0.0002913504,0.0000149785],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003134793,0.0006082182,0.1276255,0.00267395,0.001683463,0.00001524685,0.000387427,0.004174926,0.01746541,0.1702633,0.002794653,0.6691731],"study_design_scores_gemma":[0.008600789,0.0006440234,0.001097018,0.0003643282,0.001984258,0.0004190155,0.003235455,0.08813109,0.001593287,0.2606651,0.6310147,0.002250924],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9427322,0.00189142,0.04955572,0.001195938,0.001276945,0.001599198,0.000003176232,0.0001136679,0.001631757],"genre_scores_gemma":[0.9952092,0.0003234531,0.0004643565,0.0002763754,0.001756774,0.00002436972,0.00001711014,0.00006467077,0.001863723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6669222,"threshold_uncertainty_score":0.7268005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02017719713119683,"score_gpt":0.2501087863058071,"score_spread":0.2299315891746102,"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."}}