{"id":"W3037145279","doi":"10.1002/for.2717","title":"A causal model for short‐term time series analysis to predict incoming Medicare workload","year":2020,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Workload; Computer science; Term (time); Time series; Ensemble forecasting; Ensemble learning; Series (stratigraphy); Machine learning; Interval (graph theory); Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002469872,0.0001656909,0.0005873039,0.0004423941,0.0002459274,0.0002242888,0.0008909448,0.00008299664,0.00006547233],"category_scores_gemma":[0.005726126,0.0001226541,0.0004497557,0.001877593,0.00005548816,0.0003788073,0.0002090844,0.0002418582,0.00000737806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000538089,"about_ca_system_score_gemma":0.0001328564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002151361,"about_ca_topic_score_gemma":0.00001262586,"domain_scores_codex":[0.9969907,0.00004758993,0.001312028,0.0003121986,0.001042704,0.0002947218],"domain_scores_gemma":[0.9969974,0.0007946615,0.0006675483,0.0002612329,0.000844088,0.0004350976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009456613,0.0001386506,0.03673225,0.00005461248,0.001211014,0.00008293998,0.01302046,0.3629499,0.007077076,0.0005616509,0.07786796,0.4993578],"study_design_scores_gemma":[0.000153603,0.0003397919,0.0004336903,0.00009179656,0.0002934037,0.0000497553,0.000285039,0.9935487,0.0003736261,0.003030804,0.001253103,0.0001466665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2081878,0.00004876898,0.7860808,0.004841097,0.00005336575,0.0002459342,0.00006167108,0.00004859209,0.0004319721],"genre_scores_gemma":[0.800441,0.000002842698,0.1986776,0.0003080349,0.0003695685,0.00001656612,0.000002754083,0.00001620773,0.0001653677],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6305988,"threshold_uncertainty_score":0.6855121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1885171881366612,"score_gpt":0.3865930759151706,"score_spread":0.1980758877785094,"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."}}