{"id":"W2041525256","doi":"10.1139/l03-006","title":"Constraint-based genetic algorithm for earthmoving fleet selection","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"BIM and Construction Integration","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tournament selection; Crossover; Genetic algorithm; Computer science; Normalization (sociology); Selection (genetic algorithm); Fitness proportionate selection; Algorithm; Roulette; Ranking (information retrieval); Context (archaeology); Chromosome; Mathematical optimization; Fitness function; Artificial intelligence; Machine learning; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001279359,0.0001139977,0.0001326906,0.0003312202,0.00005646593,0.00005277485,0.00005790498,0.00007545436,0.0001672868],"category_scores_gemma":[0.00006421307,0.0001288208,0.00008578325,0.000165281,0.00001755615,0.0001038992,4.06087e-7,0.0001692498,0.000001991201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001555433,"about_ca_system_score_gemma":0.0003662403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004111561,"about_ca_topic_score_gemma":0.007832138,"domain_scores_codex":[0.9993593,0.000008530377,0.0002593991,0.0000609187,0.00007137879,0.0002405157],"domain_scores_gemma":[0.9994668,0.00003395292,0.00004061355,0.00005180909,0.0001347467,0.0002720456],"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":[9.310148e-7,0.000002152598,0.0003809525,0.00004760256,0.00005992625,0.00001335959,0.00005743156,0.9380956,0.002160237,0.001595769,0.0009562661,0.05662977],"study_design_scores_gemma":[0.0006229476,0.00007730696,0.0006184123,0.0001175311,0.0000386719,0.0006035516,0.00006789023,0.9191769,0.007366708,0.0001905945,0.07083771,0.0002817175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006151006,0.0006592406,0.9906181,0.000015769,0.001383851,0.00007881322,0.00001077707,0.00004228661,0.001040205],"genre_scores_gemma":[0.9555424,0.000005201312,0.04419391,0.00001980702,0.0001824481,0.000006323534,0.000001384381,0.00003047106,0.00001807802],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9493914,"threshold_uncertainty_score":0.5253158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005276082128401014,"score_gpt":0.1678410565783447,"score_spread":0.1625649744499437,"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."}}