{"id":"W4367171803","doi":"10.18280/mmep.100203","title":"Estimation and Analysis of Building Costs Using Artificial Intelligence Support Vector Machine","year":2023,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Advanced Decision-Making Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Support vector machine; Artificial intelligence; Estimation; Computer science; Machine learning; Structured support vector machine; Machine building; Relevance vector machine; Engineering; Systems engineering; Mechanical 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":[0.0005100805,0.0001274345,0.0002890056,0.0004559888,0.00005444152,0.00007847027,0.0001764473,0.00005430579,0.000001648404],"category_scores_gemma":[0.0001227811,0.0001184834,0.00004449368,0.001060639,0.00002738114,0.00019232,0.000162918,0.0000983496,0.00000126128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002531679,"about_ca_system_score_gemma":0.000007115218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000596679,"about_ca_topic_score_gemma":1.972157e-7,"domain_scores_codex":[0.9989409,0.000009912959,0.0003840103,0.0002665776,0.0002050922,0.0001934704],"domain_scores_gemma":[0.9992391,0.0003382461,0.00007422761,0.0002449371,0.00003739401,0.00006603115],"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":[8.178865e-7,0.000009548004,0.000003282239,0.00008203883,0.00002867147,0.000001876375,0.000217266,0.8149797,0.00109199,0.1671035,2.104227e-7,0.01648116],"study_design_scores_gemma":[0.00001075647,0.00002011765,0.000005298617,0.0001298204,0.00004321984,0.000005838746,0.000003050223,0.8198308,0.001354783,0.1784961,0.000001262879,0.00009894722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0479555,0.0000456645,0.9514732,0.0000332776,0.0000265875,0.000100036,0.000002504324,0.0003552556,0.000007923193],"genre_scores_gemma":[0.521224,0.0000141725,0.4787432,0.000001659673,0.000003191064,0.000004303286,0.0000012529,0.000007110427,0.000001090089],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4732685,"threshold_uncertainty_score":0.4831613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05667622890676364,"score_gpt":0.3083918974257047,"score_spread":0.2517156685189411,"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."}}