{"id":"W4411315176","doi":"10.1016/j.mlwa.2025.100689","title":"Artificial neural networks and support vector machines for more accurate cost estimation in underground mining: A contractor's viewpoint","year":2025,"lang":"en","type":"article","venue":"Machine Learning with Applications","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Mitacs","keywords":"Artificial neural network; Support vector machine; Estimation; Artificial intelligence; Computer science; Machine learning; Engineering; Data mining; Systems engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001605124,0.0001704263,0.0001941567,0.0001333763,0.0002104402,0.000114938,0.00007934422,0.00006783528,0.00000656388],"category_scores_gemma":[0.00005118169,0.000154224,0.00002298109,0.0003296603,0.00004259138,0.0001145559,0.00001759692,0.0002936443,0.000001064468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004699432,"about_ca_system_score_gemma":0.00001915358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003326107,"about_ca_topic_score_gemma":0.0001835779,"domain_scores_codex":[0.9992068,0.00001750601,0.0002599921,0.0002226662,0.00006297775,0.0002301048],"domain_scores_gemma":[0.999553,0.000188796,0.00006315888,0.0001117183,0.00003225726,0.00005101849],"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":[0.00003575982,0.00002119797,0.006237323,0.0001292385,0.00002258935,9.199219e-7,0.0001590632,0.9244718,0.0002281072,0.001696237,0.00007232581,0.06692546],"study_design_scores_gemma":[0.0003463977,0.00003037369,0.008102857,0.00004923438,0.00003158253,0.000006434992,0.00004202435,0.9883276,0.00002874235,0.0003067768,0.002566364,0.0001616215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1442992,0.0005006366,0.8517743,0.001444651,0.00007846703,0.0009943309,0.00001470778,0.0003388611,0.0005547648],"genre_scores_gemma":[0.9955598,0.00001101606,0.003106378,0.00009631557,0.00005819289,0.0007276685,0.0001858878,0.00002968696,0.0002250399],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8512605,"threshold_uncertainty_score":0.6289071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01263123738912119,"score_gpt":0.2778470495117806,"score_spread":0.2652158121226594,"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."}}