{"id":"W6957962988","doi":"10.6068/dp15999e0d5c13","title":"TREND: Xignite. FactSet Corporate Fundamentals: High Price Last 52 Weeks | Stock Symbol: RACE | Symbol Name: Ferrari N.V., 10/21/2015 - 01/12/2017. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 016-005-081Xignite. FactSet Corporate Fundamentals: Low Price Last 52 Weeks | Stock Symbol: RACE | Symbol Name: Ferrari N.V., 10/21/2015 - 01/12/2017. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 016-005-107","year":2017,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"CONQUEST; Stock (firearms); Earnings; Stock market; Stock exchange; Stock market index","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001187584,0.00265162,0.001727574,0.004210616,0.001075313,0.00376379,0.003762329,0.002771362,0.1141479],"category_scores_gemma":[0.007406701,0.0007268047,0.001378548,0.00793227,0.000397926,0.002866939,0.001788246,0.002407144,0.2255782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001824784,"about_ca_system_score_gemma":0.002827184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0384908,"about_ca_topic_score_gemma":0.06586261,"domain_scores_codex":[0.9989305,0.0001084424,0.0001097333,0.0003881508,0.0002893057,0.0001739708],"domain_scores_gemma":[0.9967368,0.0005358672,0.0003306572,0.0007168948,0.001354452,0.0003252447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002550061,0.00001152691,0.0006497765,0.0001202144,0.00001196352,0.00001083591,0.000006222878,0.0001217056,0.00003904964,0.0002090337,0.9976358,0.001158324],"study_design_scores_gemma":[0.0002480126,0.00003695604,0.005657624,0.0003016436,0.00003314416,0.000060683,0.00009602394,0.001080494,0.0003359179,0.001378153,0.9907294,0.00004189686],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001187082,0.00004339559,0.00004682655,0.00009540644,0.00004579524,0.00001382615,0.9983124,0.0004004795,0.000923213],"genre_scores_gemma":[0.0002064988,0.00002759634,0.0001346783,0.00004528086,0.00001424995,0.00003968893,0.9986708,0.00005117862,0.0008100797],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1141479,"threshold_uncertainty_score":0.3818629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06175657290179101,"score_gpt":0.3099157434653514,"score_spread":0.2481591705635604,"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."}}