{"id":"W4366000104","doi":"10.1002/ese3.1466","title":"Prediction of the stability of gob‐side entry formation by roof cutting by machine learning‐based models","year":2023,"lang":"en","type":"article","venue":"Energy Science & Engineering","topic":"Tunneling and Rock Mechanics","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geomechanica (Canada)","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Roof; Artificial neural network; Stability (learning theory); Mean squared error; Particle swarm optimization; Approximation error; Engineering; Coal mining; Computer science; Coal; Artificial intelligence; Machine learning; Structural engineering; Algorithm; Mathematics; Statistics","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":[0.000512413,0.0001143455,0.0001254566,0.0001224959,0.0001147872,0.000013261,0.0002496071,0.00005239117,0.000003154027],"category_scores_gemma":[0.000119187,0.00009807575,0.00005179092,0.0008920771,0.0000301555,0.0003151275,0.00005731228,0.0001503938,5.381128e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008170475,"about_ca_system_score_gemma":0.00002532067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004330689,"about_ca_topic_score_gemma":0.000003277045,"domain_scores_codex":[0.9989271,0.00001462212,0.0002825702,0.0001459064,0.00036752,0.0002623097],"domain_scores_gemma":[0.999573,0.00005161685,0.00006183436,0.0002063533,0.00005455303,0.00005267489],"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":[6.26479e-7,0.000004023064,0.00003402705,0.00001570687,0.000002088544,4.176743e-8,0.000144892,0.6382828,0.3605661,0.0007053673,0.00003172804,0.0002125822],"study_design_scores_gemma":[0.00006151304,0.000008503862,0.00002266874,0.00003104893,0.000003035657,4.073013e-7,0.00003206522,0.5686103,0.4306517,0.00002021949,0.0005139036,0.00004472335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5707849,0.0002597569,0.427898,0.00001670503,0.0003972771,0.0000496935,0.00004405923,0.0004007604,0.0001488335],"genre_scores_gemma":[0.9995798,0.0000434126,0.0002799553,0.00000315896,0.00001640194,0.000008061858,0.00002079338,0.00001841184,0.00003003622],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4287949,"threshold_uncertainty_score":0.3999413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0103014154227572,"score_gpt":0.1687897681342954,"score_spread":0.1584883527115382,"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."}}