{"id":"W2811131624","doi":"10.29173/iq610","title":"Business Data: Issues and Challenges from the Canadian Perspective","year":2008,"lang":"en","type":"article","venue":"IASSIST Quarterly","topic":"Financial Reporting and XBRL","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Perspective (graphical); Data science; Business; Computer science; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002186415,0.0001397331,0.0001592922,0.000067485,0.0007130213,0.0002646556,0.0003370317,0.0000619095,0.00003151584],"category_scores_gemma":[0.0002131967,0.0001026648,0.00002274658,0.0001935482,0.0001489699,0.0007093602,0.00005056682,0.0001112263,0.0001421076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004040293,"about_ca_system_score_gemma":0.000112277,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8449868,"about_ca_topic_score_gemma":0.840304,"domain_scores_codex":[0.9990221,0.000009864548,0.0001691465,0.0003693326,0.0001847907,0.0002447128],"domain_scores_gemma":[0.999059,0.00004534115,0.0001264891,0.0005196019,0.0002282018,0.00002141814],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001209255,0.0003374903,0.2193767,0.0001921327,0.0004234697,0.001846964,0.02980759,0.000003635689,0.00009074248,0.1543483,0.256232,0.33722],"study_design_scores_gemma":[0.0001265381,0.000007595727,0.7281986,0.00002263175,0.00002788487,0.000009923277,0.002971708,0.00007913632,4.667515e-7,0.003354514,0.2650536,0.0001473799],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8422321,0.01384707,0.00001911323,0.1013152,0.001012573,0.0002508381,0.00004624733,0.0001845223,0.04109234],"genre_scores_gemma":[0.9963594,0.0001812261,0.00005473882,0.00086134,0.002248823,0.000006834432,0.0000474625,0.00001784487,0.0002223403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5088219,"threshold_uncertainty_score":0.5484056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07903875406131222,"score_gpt":0.2533863650485211,"score_spread":0.1743476109872089,"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."}}