{"id":"W4313635938","doi":"10.2139/ssrn.4318145","title":"Nothing Propinks Like Propinquity: Using Machine Learning to Estimate the Effects of Spatial Proximity in the Major League Baseball Draft","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Nothing; League; Artificial intelligence; Psychology; Engineering; Computer science; Philosophy; Epistemology; Physics","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":[0.005415356,0.0006780574,0.000987798,0.001401476,0.0009757055,0.002373426,0.001494893,0.001480829,0.008080449],"category_scores_gemma":[0.01911135,0.0005886652,0.001080724,0.001659689,0.0007543816,0.001215815,0.001300422,0.002324993,0.001423331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007080829,"about_ca_system_score_gemma":0.001412892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08501904,"about_ca_topic_score_gemma":0.06376483,"domain_scores_codex":[0.998525,0.0008898422,0.00006780381,0.0002712191,0.0001128053,0.0001333228],"domain_scores_gemma":[0.979149,0.01673545,0.001602655,0.0008213209,0.0008560572,0.000835463],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001539992,0.001552003,0.7679735,0.00009881947,0.001377712,0.0003716493,0.0003332112,0.14908,0.0005294692,0.002802524,0.008625417,0.0657156],"study_design_scores_gemma":[0.0002884588,0.0005862159,0.3042038,0.00006115376,0.0004340777,0.0000682429,0.0009300238,0.6844333,0.0006292856,0.006142092,0.002166868,0.00005647503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9849124,0.0003176438,0.01060411,0.0008678112,0.0001129226,0.00005154892,0.001270655,0.0001683236,0.001694568],"genre_scores_gemma":[0.9904813,0.0001175725,0.004191925,0.00008342453,0.0001026189,0.00004420535,0.001786154,0.00003268856,0.00315998],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08501904,"threshold_uncertainty_score":0.1690484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01334987775845556,"score_gpt":0.245547589171626,"score_spread":0.2321977114131705,"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."}}