{"id":"W4410835667","doi":"10.1108/case.kellogg.2025.000006","title":"CaLNG : Peak Shaving to Alleviate a Supply-Demand Bottleneck","year":2021,"lang":"en","type":"article","venue":"Kellogg School of Management Cases","topic":"Advanced Queuing Theory Analysis","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Bottleneck; Demand management; Business; Operations management; Industrial organization; On demand; Computer science; Marketing; Economics; Commerce","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005808119,0.0003216296,0.0004793688,0.0005174508,0.0002219915,0.0002414366,0.0004544809,0.00006033227,0.002802473],"category_scores_gemma":[0.0006202854,0.0003344178,0.0002444217,0.00110438,0.0000536843,0.0009011553,0.0009207167,0.0001494591,0.00114354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006316616,"about_ca_system_score_gemma":0.00001726139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001445177,"about_ca_topic_score_gemma":0.0002165489,"domain_scores_codex":[0.9977974,0.00004558822,0.0005647221,0.0006522496,0.0004490499,0.0004910051],"domain_scores_gemma":[0.9985127,0.0001274523,0.000287505,0.0007701078,0.0002432658,0.00005898648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001352595,0.002031659,0.1990148,0.008284898,0.004555541,0.01929194,0.0003717454,0.1419244,0.0158317,0.3293278,0.2150532,0.06295972],"study_design_scores_gemma":[0.005527589,0.0001709033,0.1171856,0.003009111,0.004937174,0.0001107402,0.007633891,0.009741224,0.005704631,0.05877714,0.7829258,0.00427614],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8958,0.00144684,0.007946593,0.00280339,0.0006907964,0.0008640022,0.00001877767,0.0004648775,0.08996475],"genre_scores_gemma":[0.9849508,0.00009358118,0.002823461,0.003114033,0.0004782349,0.00004927522,0.00005870497,0.0000584562,0.008373418],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5678727,"threshold_uncertainty_score":0.9999108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01461071132666617,"score_gpt":0.243607177765705,"score_spread":0.2289964664390388,"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."}}