{"id":"W2499794885","doi":"10.1002/mma.4127","title":"Lot‐sizing policies for deterioration items under two‐level trade credit with partial trade credit to credit‐risk retailer and limited storage capacity","year":2016,"lang":"en","type":"article","venue":"Mathematical Methods in the Applied Sciences","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Trade credit; Economic order quantity; Order (exchange); Payment; Loan; Revenue; Warehouse; Business; Supply chain; Letter of credit; Revenue management; Operations research; Microeconomics; Economics; Finance; Mathematics; Marketing","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":[0.002314189,0.001119449,0.00141528,0.000714305,0.0004635201,0.00200268,0.001446318,0.00110321,0.003231288],"category_scores_gemma":[0.005426832,0.0007140951,0.001106029,0.000992285,0.001458229,0.002442191,0.0009997616,0.001243954,0.0002594149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002275382,"about_ca_system_score_gemma":0.00145368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004598838,"about_ca_topic_score_gemma":0.003079052,"domain_scores_codex":[0.9992181,0.0002418445,0.00006281216,0.0001624045,0.0001409006,0.0001739633],"domain_scores_gemma":[0.9963477,0.002123056,0.0007912799,0.0002440501,0.0002515436,0.0002423316],"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":[0.0001433911,0.00003711086,0.0006934283,0.00007425921,0.00002117744,0.00008282989,0.00003911521,0.985841,0.00137206,0.006646777,0.0001715902,0.004877098],"study_design_scores_gemma":[0.00001744789,0.00007967573,0.0005704247,0.000009162075,0.0000144309,0.0000290823,0.00001981002,0.9933923,0.0006281629,0.005097997,0.0001277311,0.00001383395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3477047,0.001089926,0.6434414,0.0004464872,0.00006662511,0.0002711144,0.00038471,0.0002573252,0.006337639],"genre_scores_gemma":[0.9843332,0.000273091,0.0137285,0.00002198431,0.00001162445,0.0000458445,0.00007375774,0.00001347314,0.001498606],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004598838,"threshold_uncertainty_score":0.01650912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1537485671193539,"score_gpt":0.3440129896720835,"score_spread":0.1902644225527296,"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."}}