{"id":"W2594812845","doi":"10.4103/jpi.jpi_65_16","title":"Open-source Software for Demand Forecasting of Clinical Laboratory Test Volumes Using Time-series Analysis","year":2017,"lang":"en","type":"article","venue":"Journal of Pathology Informatics","topic":"Clinical Laboratory Practices and Quality Control","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Calgary Laboratory Services","funders":"","keywords":"Computer science; Demand forecasting; Software; Consumables; Time series; Analytics; Data mining; Operations research; Data science; Industrial engineering; Machine learning; Engineering","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":[0.002069498,0.001981012,0.0009232343,0.004425575,0.0004603836,0.001497444,0.002038723,0.001122843,0.02955118],"category_scores_gemma":[0.01433493,0.0009242112,0.001837284,0.003100669,0.0002717825,0.00197745,0.001362985,0.001292105,0.01161617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008313111,"about_ca_system_score_gemma":0.001708747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006719908,"about_ca_topic_score_gemma":0.005668279,"domain_scores_codex":[0.9990873,0.0001365955,0.0001922414,0.0002191441,0.0003065191,0.00005816208],"domain_scores_gemma":[0.9910769,0.005920324,0.000846692,0.0007635909,0.001129578,0.0002628072],"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":[0.001051077,0.0007943398,0.02581528,0.001862988,0.0008495525,0.001158116,0.0006880797,0.1319636,0.007167348,0.009708168,0.2549972,0.5639441],"study_design_scores_gemma":[0.0003542989,0.0001250035,0.01259555,0.0002857826,0.0001507367,0.0005939374,0.000108024,0.8627614,0.009690148,0.02026496,0.09283375,0.0002364235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.01308298,0.0002973714,0.4882485,0.0004832397,0.0002321032,0.0003382242,0.02496131,0.4673774,0.004978861],"genre_scores_gemma":[0.1818433,0.001014344,0.6618139,0.0007066951,0.0003356683,0.001916193,0.08717443,0.05195669,0.01323883],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.02955118,"threshold_uncertainty_score":0.09885854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1387617516844371,"score_gpt":0.4445634410752511,"score_spread":0.305801689390814,"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."}}