{"id":"W2141452637","doi":"10.5555/2675983.2676355","title":"Utilizing simulation derived quantitative formulas for accurate excavator Hauler fleet selection","year":2013,"lang":"en","type":"article","venue":"Winter Simulation Conference","topic":"BIM and Construction Integration","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Natural Resources; University of Alberta","funders":"","keywords":"Excavator; Earthworks; Granularity; Computer science; Selection (genetic algorithm); Discrete event simulation; Field (mathematics); Production (economics); Duration (music); Industrial engineering; Operations research; Engineering; Simulation; Civil engineering; Artificial intelligence; Mathematics","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":[0.001489528,0.0007687543,0.0005523623,0.001374722,0.0003194806,0.001175594,0.0008820721,0.0004656478,0.004071294],"category_scores_gemma":[0.00737852,0.0004177307,0.0005210722,0.001123963,0.0003917236,0.001651708,0.0005958851,0.0007838902,0.0007935861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00120758,"about_ca_system_score_gemma":0.001194572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006547355,"about_ca_topic_score_gemma":0.007462597,"domain_scores_codex":[0.999332,0.0001890382,0.00006485509,0.00007452967,0.0002837729,0.00005583367],"domain_scores_gemma":[0.9970899,0.001663177,0.0003055384,0.0002505001,0.0006541004,0.00003673139],"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":[0.000008423484,0.00001487689,0.0006656215,0.00004095051,0.000005702203,0.00002069287,0.00002977148,0.9538622,0.001026317,0.02463956,0.0004931707,0.01919262],"study_design_scores_gemma":[0.000002143264,0.000007988957,0.0001262957,0.00001308258,0.000003390152,0.00001448518,0.00001322889,0.990712,0.001032828,0.006210134,0.001858184,0.000006305637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007141463,0.00005306799,0.9856711,0.0000536724,0.00001970545,0.00003078794,0.0001849942,0.0002930401,0.006552253],"genre_scores_gemma":[0.5301154,0.0005689794,0.4629376,0.00007911789,0.00003219607,0.0003236342,0.0008393811,0.0004772239,0.004626498],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006547355,"threshold_uncertainty_score":0.01361984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05505243749383319,"score_gpt":0.2997921766228763,"score_spread":0.2447397391290431,"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."}}