{"id":"W4393199887","doi":"10.1080/00207543.2024.2333108","title":"Low-Carbon supply chain optimisation with carbon emission reduction level and warranty period: nash bargaining fairness concern","year":2024,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Stackelberg competition; Warranty; Supply chain; Bargaining problem; Microeconomics; Revenue sharing; Revenue; Nash equilibrium; Business; Game theory; Reduction (mathematics); Bargaining power; Backward induction; Environmental economics; Economics; Marketing; Finance","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":[],"consensus_categories":[],"category_scores_codex":[0.003517183,0.0001881816,0.0001937256,0.00174772,0.0001904278,0.0009417516,0.0003567122,0.00007721358,0.00009378839],"category_scores_gemma":[0.0005672898,0.000156389,0.00005724066,0.0008070936,0.0001970463,0.001443038,0.0002198341,0.000626453,0.000005523784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000505529,"about_ca_system_score_gemma":0.0002065007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003805346,"about_ca_topic_score_gemma":0.00001356407,"domain_scores_codex":[0.9968132,0.00008991079,0.0004789112,0.0004493895,0.001835309,0.0003332181],"domain_scores_gemma":[0.9969451,0.00005572338,0.0002422652,0.0001732771,0.002545561,0.00003806657],"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":[0.008402522,0.001299378,0.04030949,0.004620735,0.002925477,0.003921885,0.01798419,0.04099039,0.1825757,0.03718878,0.02662145,0.63316],"study_design_scores_gemma":[0.01068223,0.001343395,0.02294965,0.01355174,0.0006524696,0.004709038,0.1794708,0.4936986,0.05835368,0.04748819,0.1637567,0.003343499],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9729257,0.0005067197,0.0005647576,0.01990328,0.002693673,0.0004508305,0.000001531261,0.00007437568,0.002879164],"genre_scores_gemma":[0.9912556,0.0001330978,0.0002692033,0.00004243496,0.006077287,0.00002429409,0.00001722639,0.00004499843,0.002135809],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6298165,"threshold_uncertainty_score":0.9081333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06318515016945009,"score_gpt":0.3317046511848858,"score_spread":0.2685195010154357,"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."}}