{"id":"W3122217516","doi":"10.2139/ssrn.3141526","title":"Improving Investment Operations Through Data Science: A Case Study of Innovation in Valuation","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Merck Canada Inc. (Canada)","funders":"","keywords":"Valuation (finance); Business; Industrial organization; Economics; Finance","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":[0.01545945,0.0005465934,0.0004638576,0.001890904,0.001142509,0.003706733,0.001456133,0.002096905,0.001689773],"category_scores_gemma":[0.05533054,0.0001887804,0.0005829897,0.002700257,0.001812216,0.003954338,0.001734709,0.001998475,0.0002417538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001976765,"about_ca_system_score_gemma":0.002391245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003343565,"about_ca_topic_score_gemma":0.003162202,"domain_scores_codex":[0.9947149,0.00324645,0.0002716717,0.0002522586,0.001151754,0.0003629342],"domain_scores_gemma":[0.9032428,0.08225034,0.002661687,0.005843815,0.004767004,0.001234327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"case_report","study_design_scores_codex":[0.001260112,0.003375134,0.1054322,0.0008038805,0.0002322015,0.003998421,0.009711476,0.1060139,0.006358349,0.1123732,0.00649186,0.6439493],"study_design_scores_gemma":[0.0005301451,0.002893597,0.05141846,0.0007274495,0.0003856387,0.002290284,0.01118423,0.6604919,0.03386945,0.1905981,0.04536209,0.0002487294],"study_design_candidate":"case_report","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8809476,0.001011436,0.08953617,0.005215919,0.00008265577,0.0003331537,0.0001804371,0.0002122876,0.02248042],"genre_scores_gemma":[0.9578786,0.0003426754,0.04063478,0.00008162503,0.00002934734,0.00003901467,0.00006694331,0.00002462937,0.0009022638],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01545945,"threshold_uncertainty_score":0.08175838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2580596408532044,"score_gpt":0.4880108320596135,"score_spread":0.2299511912064091,"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."}}