{"id":"W2096613931","doi":"10.1139/t09-124","title":"An efficient approach for locating the critical slip surface in slope stability analyses using a real-coded genetic algorithm","year":2010,"lang":"en","type":"article","venue":"Canadian Geotechnical Journal","topic":"Geotechnical Engineering and Analysis","field":"Engineering","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Slip (aerodynamics); Slope stability analysis; Safety factor; Slope stability; Algorithm; Factor of safety; Genetic algorithm; Stability (learning theory); Search algorithm; Mathematical optimization; Computer science; Mathematics; Engineering; Geotechnical engineering; Machine learning","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":[0.001269008,0.0002667045,0.0003855933,0.0002480994,0.0003133857,0.000192878,0.0005923935,0.000319749,0.00004715886],"category_scores_gemma":[0.0005110063,0.0002173615,0.0002133334,0.0007258556,0.0001761265,0.00008122748,0.00002610619,0.001623564,0.000001798957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003419798,"about_ca_system_score_gemma":0.0002762531,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009618527,"about_ca_topic_score_gemma":0.002586919,"domain_scores_codex":[0.997808,0.00008146287,0.0006199031,0.00033458,0.0002746646,0.0008814327],"domain_scores_gemma":[0.9981228,0.0002355671,0.00004410183,0.0005663596,0.0001601496,0.0008710845],"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":[0.000002082347,0.00003512159,0.00004605031,0.00002289558,0.00002253871,0.00001685012,0.00003936522,0.9821721,0.01279624,0.00003970967,0.00001525518,0.004791776],"study_design_scores_gemma":[0.0001822707,0.00003195595,0.001232817,0.0000210491,0.00007264079,0.0001435656,0.0001303959,0.9972239,0.0004627395,0.0001088746,0.00011052,0.0002792818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3054091,0.0001319078,0.6938007,0.0001473788,0.0001542666,0.0001889409,0.0000279484,0.0001134682,0.00002627616],"genre_scores_gemma":[0.8835564,0.00001410264,0.116126,0.00003574281,0.0001977108,0.00001432719,0.000006494718,0.00004774171,0.000001469993],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5781472,"threshold_uncertainty_score":0.9969765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02927683078182444,"score_gpt":0.2832224226643488,"score_spread":0.2539455918825244,"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."}}