{"id":"W6995923920","doi":"","title":"Predictive models can lose the plot. Here's how to keep them on track","year":2023,"lang":"en","type":"article","venue":"CERES (Cranfield University)","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pace; Analytics; Track (disk drive); Launched; Predictive analytics; Quarter (Canadian coin); Online algorithm; Business model; Information technology; Technology forecasting","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":[0.0001000861,0.0001731175,0.0001426476,0.000239575,0.0003263784,0.0002076082,0.0007615506,0.0000940703,0.0002838032],"category_scores_gemma":[0.00006091309,0.0001355516,0.00008046485,0.001365109,0.00006941547,0.0007326297,0.0003271562,0.0001992347,0.0005377643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003177563,"about_ca_system_score_gemma":0.00002129513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004612273,"about_ca_topic_score_gemma":0.0005120632,"domain_scores_codex":[0.9990165,0.00001349054,0.00008262982,0.0003386278,0.0002365517,0.000312248],"domain_scores_gemma":[0.9992688,0.0001045311,0.00006372476,0.0004147994,0.0001229185,0.00002525398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001382925,0.0003661049,0.007013903,0.0003718235,0.0003298782,0.0005722014,0.001925258,0.02299923,0.0003632923,0.5137835,0.3583337,0.0925582],"study_design_scores_gemma":[0.0008396734,0.00007329019,0.005687297,0.0002240111,0.0001898559,0.000003237436,0.01285753,0.02773544,0.0006792403,0.01446155,0.9364148,0.0008340021],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.525758,0.00002205323,0.007085432,0.04785404,0.001406779,0.0009635105,0.0000969321,0.0009581686,0.4158551],"genre_scores_gemma":[0.9847712,0.00004310389,0.00001186839,0.001971463,0.0004946268,0.000002135029,0.00002905522,0.00001891565,0.01265766],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5780811,"threshold_uncertainty_score":0.6912052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09812134343303225,"score_gpt":0.2292767621662081,"score_spread":0.1311554187331759,"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."}}