{"id":"W4293041683","doi":"10.2139/ssrn.4198438","title":"Estimating the Value of Offsite Data to Advertisers on Meta","year":2022,"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":"Kellogg's (Canada)","funders":"","keywords":"Value (mathematics); Business; Advertising; Statistics; Mathematics","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":[0.006945986,0.0007156432,0.0006327472,0.005902434,0.0004500225,0.003505217,0.001272598,0.001884199,0.004101205],"category_scores_gemma":[0.06406071,0.0004745563,0.0008582547,0.003911619,0.0005462621,0.004019509,0.0008495674,0.001290264,0.0007029712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002145797,"about_ca_system_score_gemma":0.00106702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009617551,"about_ca_topic_score_gemma":0.007348483,"domain_scores_codex":[0.9969229,0.001696912,0.0001641762,0.0003614056,0.0006534868,0.0002010575],"domain_scores_gemma":[0.886905,0.09698635,0.004355651,0.006987102,0.00364152,0.001124318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004959079,0.001243727,0.5816309,0.0004131492,0.0009943815,0.0006940882,0.0002988599,0.1771206,0.003729918,0.01872813,0.009492649,0.2006946],"study_design_scores_gemma":[0.0001728596,0.0005714073,0.1174368,0.0001118749,0.0005984997,0.0003638975,0.0006746597,0.8523934,0.006806992,0.01710144,0.003696127,0.00007203325],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9760311,0.0009362921,0.00845548,0.001304772,0.00007084441,0.00007216656,0.004439418,0.0003153248,0.008374576],"genre_scores_gemma":[0.9926037,0.0001781986,0.004569368,0.00006538709,0.00006982333,0.00001117132,0.001470393,0.00002636879,0.001005516],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009617551,"threshold_uncertainty_score":0.03673428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03829696169459567,"score_gpt":0.2718100023838586,"score_spread":0.2335130406892629,"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."}}