{"id":"W4389125385","doi":"10.2139/ssrn.4626231","title":"KPI Information Acquisition by Analysts: Evidence from Conference Call","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Auditing, Earnings Management, Governance","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Call centre; Business; Data science; Computer science; Telecommunications","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":[0.007413726,0.0003734398,0.0003860115,0.003319877,0.002202581,0.006915287,0.001520401,0.003274539,0.01236693],"category_scores_gemma":[0.09205233,0.0003633233,0.0002250283,0.002235311,0.0008792974,0.003478462,0.002573823,0.003021189,0.003272698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002486463,"about_ca_system_score_gemma":0.003208222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01650827,"about_ca_topic_score_gemma":0.01743569,"domain_scores_codex":[0.9901502,0.002909726,0.0003895357,0.0007486875,0.004467231,0.001334638],"domain_scores_gemma":[0.8038864,0.1226474,0.03788062,0.006633713,0.01931614,0.009635767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005423453,0.003067557,0.7701877,0.0004928949,0.0002074568,0.001178911,0.01789111,0.0007150524,0.003444527,0.002742606,0.03183411,0.1628146],"study_design_scores_gemma":[0.0001517165,0.0008410827,0.9474376,0.0002799056,0.0001494642,0.0005746842,0.02487227,0.001748052,0.002938646,0.001057218,0.01979067,0.0001587588],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9663584,0.0007726708,0.0002072143,0.001982884,0.00006500704,0.00009224203,0.0005725087,0.00008487092,0.02986444],"genre_scores_gemma":[0.9932945,0.0003700584,0.0001559502,0.0005380724,0.0001009975,0.00005902819,0.0004506304,0.00002766058,0.005002998],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01650827,"threshold_uncertainty_score":0.04137146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00900182783230163,"score_gpt":0.2174112177093221,"score_spread":0.2084093898770205,"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."}}