{"id":"W4385945676","doi":"10.2139/ssrn.4544139","title":"Multi-Objective Particle Swarm Optimization Algorithm Based on Multiple Adaptive Methods for Fire Trucks Dispatching in Mixed Uncertain Forest Fire Environments","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Truck; Particle swarm optimization; Computer science; Swarm behaviour; Mathematical optimization; Algorithm; Environmental science; Engineering; Automotive engineering; Mathematics; Artificial intelligence","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":[0.0009561782,0.0008211827,0.0009788027,0.0004662384,0.0004492368,0.0007635367,0.0008719084,0.001115661,0.001168908],"category_scores_gemma":[0.002177784,0.000523898,0.0007317153,0.000587969,0.0004627849,0.0007031066,0.0006926028,0.00113383,0.0001260758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004558698,"about_ca_system_score_gemma":0.0008400676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008545324,"about_ca_topic_score_gemma":0.005048079,"domain_scores_codex":[0.9997769,0.0000952898,0.00001282288,0.00003727519,0.00005354329,0.00002410547],"domain_scores_gemma":[0.9993555,0.0004328765,0.00006687066,0.00002171874,0.00009856122,0.00002449722],"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":[0.0000331708,0.00001825,0.0002086408,0.00002435928,0.00002500083,0.00002146876,0.0000187732,0.9862248,0.0003498284,0.001331269,0.0001803497,0.01156411],"study_design_scores_gemma":[0.000002839982,0.000007205792,0.0000302204,0.000001216288,0.000001859249,0.000001367559,0.000001722635,0.9996973,0.00003239443,0.0001837659,0.00003918333,9.610052e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03048791,0.0003034973,0.9667822,0.0001558994,0.0000990634,0.00004558053,0.00002053919,0.00008200122,0.002023326],"genre_scores_gemma":[0.7447454,0.000390541,0.2506607,0.00007899365,0.0001001194,0.0002730532,0.00008517636,0.00005374153,0.003612212],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008545324,"threshold_uncertainty_score":0.0169912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02961420362802267,"score_gpt":0.3101684961734176,"score_spread":0.280554292545395,"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."}}