{"id":"W2964913780","doi":"10.1016/j.omega.2019.07.009","title":"Strategic and operational decision-making in expanding supply chains for LNG as a fuel","year":2019,"lang":"en","type":"article","venue":"Omega","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta; Natural Sciences and Engineering Research Council of Canada","keywords":"Bunker; Truck; Liquefied natural gas; Supply chain; Operations research; Greenhouse gas; Offset (computer science); Transport engineering; Engineering; Computer science; Natural gas; Waste management; Business; Automotive engineering; Coal","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000312323,0.00009000221,0.0001238933,0.0001309778,0.00003499391,0.00007976834,0.00006234265,0.00006019692,0.00008289538],"category_scores_gemma":[0.00008282125,0.00009544825,0.00002050351,0.0001548344,0.000007245566,0.0001514122,0.00001984187,0.00008177453,0.00001298154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005308147,"about_ca_system_score_gemma":0.00002153058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002983169,"about_ca_topic_score_gemma":0.000009935015,"domain_scores_codex":[0.9994028,0.00001763781,0.0001683178,0.000149703,0.00009211763,0.0001694787],"domain_scores_gemma":[0.9994981,0.0003337091,0.00001679091,0.00009570058,0.0000233704,0.0000323675],"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":[0.00003684175,0.00001945369,0.02237315,0.0001612345,0.00002165771,0.00000549305,0.001333809,0.9120139,0.006777755,0.04509673,0.00006157118,0.01209842],"study_design_scores_gemma":[0.0008236084,0.00003447028,0.005039234,0.0001603866,0.00000367389,0.00001148125,0.0004015378,0.988758,0.0002245289,0.004018869,0.0003325525,0.000191616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9156696,0.0001747621,0.07857774,0.00003929724,0.0002046671,0.0002687647,0.000006744698,0.00008005467,0.004978341],"genre_scores_gemma":[0.8871711,0.00002013239,0.112579,0.0000414304,0.0000450203,0.00002300719,0.000006049316,0.00002420507,0.00009004852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07674415,"threshold_uncertainty_score":0.3892267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01847117339076021,"score_gpt":0.3027536799878898,"score_spread":0.2842825065971296,"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."}}