{"id":"W1760697510","doi":"10.1139/cjce-2013-0223","title":"Combining case-based reasoning with genetic algorithm optimization for preliminary cost estimation in construction industry","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Case-based reasoning; Genetic algorithm; Computer science; Cost estimate; Matching (statistics); Identification (biology); Similarity (geometry); Contrast (vision); Estimation; Data mining; Mathematical optimization; Machine learning; Artificial intelligence; Engineering; Mathematics; Statistics; Systems engineering","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":[0.0002468216,0.0001126047,0.0001409613,0.0004435511,0.0001025781,0.0001714155,0.0001612332,0.0001097758,0.00001506066],"category_scores_gemma":[0.00009877469,0.0001171467,0.00002532451,0.0003059949,0.00001740204,0.0005062909,0.000005425811,0.0003287888,4.277799e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001695883,"about_ca_system_score_gemma":0.0006149579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009134665,"about_ca_topic_score_gemma":0.001283257,"domain_scores_codex":[0.9992258,0.00002213723,0.0002760101,0.0001172121,0.00009604681,0.0002627755],"domain_scores_gemma":[0.9991654,0.0001259275,0.0001688564,0.0001098893,0.0001676125,0.0002623085],"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":[0.000002072748,0.000002390178,0.00176088,0.000022804,0.000007821026,0.000248117,0.0002083606,0.9802183,0.000004138267,0.00006796214,0.00005680854,0.01740033],"study_design_scores_gemma":[0.0005003894,0.0001642648,0.001223459,0.000574796,0.000009325084,0.002414568,0.00005562786,0.9947985,0.00005412394,0.00003306427,0.00003663613,0.0001352156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01834594,0.000146298,0.9808907,0.000157493,0.0002103036,0.0001943798,0.00000252515,0.00002064679,0.000031718],"genre_scores_gemma":[0.5254452,3.628681e-7,0.4744876,0.00001953826,0.00002249811,0.00001151643,0.00000247849,0.000009113148,0.000001652619],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5070993,"threshold_uncertainty_score":0.4777106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006788192131569717,"score_gpt":0.1858520833617242,"score_spread":0.1790638912301545,"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."}}