{"id":"W2725529828","doi":"10.4050/f-0073-2017-12219","title":"Demand Forecasting: Cross-Functional, Cross-Disciplinary Analytics","year":2017,"lang":"en","type":"article","venue":"","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Cross disciplinary; Analytics; Computer science; Data science","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":[0.002551973,0.0008064762,0.0005580117,0.002751882,0.0004067724,0.002940364,0.0007538042,0.0009550914,0.002296882],"category_scores_gemma":[0.007214759,0.0003224267,0.0005619994,0.005191451,0.0004436241,0.004423535,0.001602569,0.001317036,0.00109172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007896929,"about_ca_system_score_gemma":0.0006949092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003339333,"about_ca_topic_score_gemma":0.002293917,"domain_scores_codex":[0.9990028,0.0002980941,0.00008315459,0.0001849807,0.0003635381,0.00006745956],"domain_scores_gemma":[0.9975621,0.001019675,0.0003700522,0.0004039871,0.0005211461,0.0001230976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001455788,0.0002494344,0.06430963,0.0004019479,0.0002305992,0.0004601683,0.0008309029,0.1335303,0.004865397,0.07039461,0.01868291,0.7058985],"study_design_scores_gemma":[0.00001122542,0.0001295832,0.02804922,0.000168769,0.00006566801,0.0003078439,0.001101423,0.8037923,0.002883311,0.1337074,0.02969481,0.00008830809],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1091434,0.005214837,0.845012,0.007664174,0.0004079496,0.0001522822,0.002391111,0.002472399,0.02754188],"genre_scores_gemma":[0.8393558,0.004242478,0.1483596,0.0004285258,0.0005159485,0.0001093001,0.003246917,0.0001676773,0.003573808],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003339333,"threshold_uncertainty_score":0.01349628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4559676507881958,"score_gpt":0.5012675728310815,"score_spread":0.04529992204288563,"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."}}