{"id":"W2137507954","doi":"10.1139/x08-017","title":"RuttOpt — a decision support system for routing of logging trucks","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Truck; Gantt chart; Computer science; Scheduling (production processes); Logging; Decision support system; Schedule; Database; Range (aeronautics); Vehicle routing problem; Time horizon; Information system; Operations research; Routing (electronic design automation); Data mining; Engineering; Mathematical optimization; Operations management; Systems engineering; Geography; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008720131,0.000820437,0.0008786476,0.0009024694,0.0004299321,0.001164898,0.001860667,0.000597787,0.02596781],"category_scores_gemma":[0.002329289,0.000550178,0.000578763,0.001033787,0.0001898093,0.001095889,0.0006973084,0.0006917844,0.005017413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006311084,"about_ca_system_score_gemma":0.001350763,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007545031,"about_ca_topic_score_gemma":0.007005858,"domain_scores_codex":[0.9994544,0.0001275667,0.00005784403,0.0001361118,0.0001801602,0.00004399144],"domain_scores_gemma":[0.9988496,0.0005178446,0.0001185704,0.0001733883,0.0002538467,0.00008669246],"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":[0.0008932442,0.000379803,0.002688255,0.0006914223,0.0001448253,0.0004797681,0.0002294415,0.2438847,0.01353306,0.007274095,0.1241651,0.6056362],"study_design_scores_gemma":[0.0003736118,0.0001833877,0.001580494,0.00007196757,0.00005434398,0.0003142584,0.0000764739,0.8939888,0.009258343,0.005212957,0.088798,0.00008733627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03659472,0.0004802001,0.7440693,0.0003524915,0.0001453712,0.0006963097,0.0110587,0.1907087,0.01589421],"genre_scores_gemma":[0.2349125,0.0006560136,0.7252398,0.0002732171,0.00009368094,0.001004319,0.02143899,0.003670292,0.01271114],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9924549,"threshold_uncertainty_score":0.08687097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06825843467581155,"score_gpt":0.3391481718268239,"score_spread":0.2708897371510124,"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."}}