{"id":"W2126365332","doi":"10.1002/nag.566","title":"Prediction of surface crown pillar stability using artificial neural networks","year":2006,"lang":"en","type":"article","venue":"International Journal for Numerical and Analytical Methods in Geomechanics","topic":"Rock Mechanics and Modeling","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Pillar; Overburden; Crown (dentistry); Stability (learning theory); Artificial neural network; Perceptron; Geotechnical engineering; Structural engineering; Rock mass classification; Geology; Engineering; Artificial intelligence; Computer science; Materials science; Composite material; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0008840061,0.000135312,0.0002717569,0.0001037569,0.00006071202,0.00005739789,0.0001472938,0.0001132193,0.00001878018],"category_scores_gemma":[0.0001436565,0.0001247051,0.0001331413,0.0001591641,0.00001538889,0.0001173251,0.00004969525,0.0002976783,9.712465e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001042153,"about_ca_system_score_gemma":0.00001443674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002365675,"about_ca_topic_score_gemma":0.000002269346,"domain_scores_codex":[0.9986402,0.00006916079,0.0006538371,0.0001642295,0.0002323563,0.0002402623],"domain_scores_gemma":[0.999332,0.0002172932,0.00008801858,0.00007153796,0.0002054875,0.00008569695],"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":[0.0000665325,0.00005800936,0.00008255571,0.00001272721,0.00004347472,0.00000447692,0.00001205484,0.9586521,0.0147377,0.0110132,0.000009249861,0.01530796],"study_design_scores_gemma":[0.0002301927,0.00005732777,0.00002343798,0.0000225818,0.0000273923,0.00003909147,0.00003124696,0.9483576,0.002919671,0.04803902,0.0001520014,0.0001004198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1647392,0.0002183839,0.8335391,0.00005315547,0.001317583,0.00007182161,0.00002396579,0.00002042124,0.00001634849],"genre_scores_gemma":[0.8680919,0.00006939306,0.1314577,0.0000157469,0.0003360424,0.000001521909,0.000005951988,0.00001867828,0.00000306486],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7033527,"threshold_uncertainty_score":0.5085326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06677204003577311,"score_gpt":0.3454788160347828,"score_spread":0.2787067759990096,"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."}}