{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002061351,0.0007263183,0.0007218751,0.00113113,0.0004702093,0.0009081998,0.001315868,0.001063129,0.002643852],"category_scores_gemma":[0.005238988,0.000447399,0.001044789,0.0008120199,0.0003579156,0.0009997345,0.000436468,0.001495756,0.0003097122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009549078,"about_ca_system_score_gemma":0.001220566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02309903,"about_ca_topic_score_gemma":0.01729122,"domain_scores_codex":[0.9994906,0.0001633411,0.00003272991,0.0001376503,0.0001002117,0.00007534268],"domain_scores_gemma":[0.9974269,0.001861516,0.0002560794,0.00007769604,0.0002999564,0.00007779058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005713446,0.00007551702,0.004670095,0.00003522275,0.00008945609,0.00009931923,0.00005202459,0.9708581,0.0005771218,0.006183087,0.0006287129,0.01667421],"study_design_scores_gemma":[0.000001056626,0.000004997125,0.0002329753,0.000001647985,0.000005002141,0.000003643841,0.00000239121,0.9990336,0.00004433386,0.0006183469,0.00005010518,0.00000188762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1717364,0.0008354597,0.8222736,0.001040462,0.0002047641,0.00008537815,0.0005308097,0.000619425,0.00267379],"genre_scores_gemma":[0.9690693,0.0005051954,0.02708094,0.00008736777,0.0001276216,0.0001042346,0.000344849,0.00002862744,0.002651756],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02309903,"threshold_uncertainty_score":0.04592919,"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."}}