{"id":"W2058209693","doi":"10.1016/j.mcm.2005.09.027","title":"An <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si1.gif\" display=\"inline\" overflow=\"scroll\"><mml:mrow><mml:mo>(</mml:mo><mml:mi>s</mml:mi><mml:mo>,</mml:mo><mml:mi>Q</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math> Markovian inventory system with lost sales and two demand classes","year":2006,"lang":"lv","type":"article","venue":"Mathematical and Computer Modelling","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scroll; Algorithm; Computer science; Artificial intelligence; Mathematics; Theology; Philosophy","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007988738,0.00113878,0.0008203556,0.001811481,0.0007793408,0.004400702,0.002185275,0.001593644,0.4969293],"category_scores_gemma":[0.003102257,0.00100676,0.001122002,0.003010452,0.0005189587,0.004056878,0.001846188,0.00225974,0.3023138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001656775,"about_ca_system_score_gemma":0.001225583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007952006,"about_ca_topic_score_gemma":0.007506928,"domain_scores_codex":[0.9995104,0.00009409779,0.00004734129,0.00008827118,0.0002170927,0.0000428157],"domain_scores_gemma":[0.998975,0.0003440974,0.00005701908,0.0002334923,0.0003235567,0.00006676232],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006570626,0.00005863675,0.0001685973,0.0002830937,0.00001381366,0.00008978565,0.0001216064,0.00228025,0.002372523,0.1279376,0.7824631,0.08414527],"study_design_scores_gemma":[0.00003278131,0.00001197269,0.000236455,0.00005906006,0.00000672794,0.0001226005,0.00002924985,0.01233317,0.002748673,0.029677,0.9547156,0.00002664776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001410239,0.0004463891,0.5238935,0.003627765,0.001181833,0.0002945618,0.06660555,0.05395119,0.3485891],"genre_scores_gemma":[0.02575427,0.001983698,0.3584283,0.001525614,0.0006749239,0.0008126964,0.08905771,0.03842094,0.4833418],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4969293,"threshold_uncertainty_score":0.7175694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01767620472665891,"score_gpt":0.2159570466536388,"score_spread":0.1982808419269799,"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."}}