{"id":"W6891953120","doi":"10.48683/1926.00117702","title":"Driving superior demand forecasting accuracy by incorporating customers and prospects behavior outside the firm environment","year":2022,"lang":"en","type":"article","venue":"CentAUR (University of Reading)","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Unobservable; Demand forecasting; Competitor analysis; Product (mathematics); Benchmark (surveying); Data collection; Key (lock); New product development","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.001065362,0.000110714,0.0001817545,0.0001055625,0.001566539,0.00006980626,0.0006319429,0.00003159641,0.0002291871],"category_scores_gemma":[0.0001717997,0.0001009903,0.00007388149,0.0003528646,0.0002755281,0.000235583,0.0009044969,0.0002039874,0.000006958708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001195128,"about_ca_system_score_gemma":0.00002811663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001788424,"about_ca_topic_score_gemma":0.00003441391,"domain_scores_codex":[0.99856,0.00008783841,0.0002141986,0.0003750762,0.0005618361,0.000201056],"domain_scores_gemma":[0.9986787,0.000462259,0.0003851928,0.0003482134,0.00003753493,0.00008805398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006433172,0.000396331,0.7477899,0.00002679454,0.00005688469,0.0000673344,0.01447132,0.0007333141,0.02977178,0.00563693,0.03614671,0.1648384],"study_design_scores_gemma":[0.003753203,0.00140693,0.2216058,0.000234244,0.0006341981,0.0006059762,0.2507088,0.1264856,0.007303118,0.01502772,0.3696808,0.002553697],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957529,0.00005584072,0.002157304,0.0006667894,0.00004010928,0.0004268692,0.00004323497,0.00004317724,0.0008138061],"genre_scores_gemma":[0.9953676,0.00001418064,0.003734438,0.0000309386,0.00001028754,0.000006854736,0.00001152878,0.000009585982,0.0008145398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.526184,"threshold_uncertainty_score":0.9997333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04723973569669736,"score_gpt":0.2698455488277877,"score_spread":0.2226058131310903,"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."}}