{"id":"W2055967274","doi":"10.1179/mnt.2002.111.1.73","title":"Application of a neural network in the empirical design of underground excavation spans","year":2002,"lang":"en","type":"article","venue":"Mining Technology Transactions of the Institutions of Mining and Metallurgy Section A","topic":"Rock Mechanics and Modeling","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Rocky Mountain Research Station","keywords":"Excavation; Artificial neural network; Rock mass classification; Span (engineering); Grid; Range (aeronautics); Stability (learning theory); Computer science; Engineering; Graph; Data mining; Artificial intelligence; Structural engineering; Civil engineering; Geology; Geotechnical engineering; Machine learning; Theoretical computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002339062,0.000526154,0.000439528,0.001695186,0.0002996766,0.0007050527,0.0006905309,0.0006372119,0.001659315],"category_scores_gemma":[0.01193989,0.0003801923,0.0002771042,0.001053362,0.0004121808,0.0008529332,0.0005895267,0.0004531574,0.0001542892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00102825,"about_ca_system_score_gemma":0.0008758006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00970865,"about_ca_topic_score_gemma":0.009866724,"domain_scores_codex":[0.9990911,0.0004807139,0.0000630865,0.0001606492,0.0001567874,0.00004761896],"domain_scores_gemma":[0.9947649,0.004153143,0.0002645475,0.0001693382,0.0005707103,0.0000772343],"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":[0.0001601463,0.000111725,0.01414893,0.00006870277,0.00005887583,0.00009738398,0.0001613634,0.8426436,0.001092457,0.002004863,0.0004138426,0.1390382],"study_design_scores_gemma":[0.000006752003,0.0000220921,0.001297036,0.00000918422,0.00000473392,0.00001151746,0.00002493636,0.9972427,0.0003520586,0.0008692158,0.0001555863,0.000004226681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3807199,0.0002072751,0.6144879,0.0002712217,0.00001455405,0.0002176804,0.0002888781,0.0004890295,0.003303399],"genre_scores_gemma":[0.821059,0.00008855439,0.1775752,0.00002745692,0.000008984086,0.0001487608,0.0002218085,0.00002616749,0.000844116],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00970865,"threshold_uncertainty_score":0.01930428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0828494390517519,"score_gpt":0.258357632061586,"score_spread":0.1755081930098341,"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."}}