{"id":"W4233117717","doi":"10.17771/pucrio.acad.35069","title":"PROJETO DA CADEIA DE SUPRIMENTOS DE HIDROGÊNIO: UMA METODOLOGIA PARA O PLANEJAMENTO SOB INCERTEZA","year":2013,"lang":"pt","type":"dissertation","venue":"","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nexen (Canada)","funders":"","keywords":"Commercialization; Context (archaeology); Supply chain; Computer science; Integer programming; Stochastic programming; Energy carrier; Work (physics); Linear programming; Operations research; Mathematical optimization; Engineering; Business; Mathematics; Renewable energy","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":[0.002072226,0.0009528102,0.0006746273,0.001263754,0.001011847,0.002943713,0.001734033,0.001683053,0.004456443],"category_scores_gemma":[0.004504743,0.0005808848,0.001143578,0.001874365,0.000872963,0.002725251,0.00212558,0.001465204,0.0009418659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001118558,"about_ca_system_score_gemma":0.002266596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004313897,"about_ca_topic_score_gemma":0.003475181,"domain_scores_codex":[0.9990866,0.0002550434,0.00005930602,0.0002373992,0.0002880811,0.00007351136],"domain_scores_gemma":[0.9983462,0.0007245553,0.0001934472,0.0002028421,0.0004164568,0.0001165954],"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":[0.0006702258,0.0008315044,0.02437801,0.002387179,0.0001872571,0.000887402,0.003818673,0.2673306,0.03634132,0.1053061,0.006663346,0.5511984],"study_design_scores_gemma":[0.0001239238,0.001473594,0.0111543,0.000492361,0.000233369,0.000913034,0.003026913,0.8047956,0.04184091,0.05921404,0.07657458,0.0001573118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1233655,0.002031362,0.8489729,0.001307633,0.0001550699,0.0004672736,0.0005647231,0.0007923906,0.02234315],"genre_scores_gemma":[0.5588053,0.003173684,0.4187373,0.0002195559,0.00007492623,0.0005702548,0.0007464356,0.000176715,0.01749578],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004456443,"threshold_uncertainty_score":0.01490825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03225878421122276,"score_gpt":0.2842187627149813,"score_spread":0.2519599785037586,"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."}}