{"id":"W4402926018","doi":"10.1139/cgj-2023-0215","title":"Inversion of short-term precursor of acoustic emission in uniaxial compression based on SOM neural network","year":2024,"lang":"en","type":"article","venue":"Canadian Geotechnical Journal","topic":"Geoscience and Mining Technology","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Acoustic emission; Artificial neural network; Geology; Term (time); Inversion (geology); Geotechnical engineering; Seismology; Materials science; Computer science; Composite material; Artificial intelligence; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002484967,0.0004184612,0.0001951104,0.0003819076,0.0001395707,0.00024723,0.0003413211,0.0003038059,0.0005881913],"category_scores_gemma":[0.0007096048,0.0001922689,0.0002681564,0.0003616233,0.0002136951,0.0005081936,0.0002539374,0.000343448,0.00008641498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002038009,"about_ca_system_score_gemma":0.000368617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003723905,"about_ca_topic_score_gemma":0.003623458,"domain_scores_codex":[0.9999287,0.000009829189,0.000003829592,0.00001555824,0.00002773287,0.00001429907],"domain_scores_gemma":[0.9998598,0.00005066433,0.0000167358,0.000007181688,0.0000564967,0.00000909195],"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":[0.0004138706,0.0001270651,0.01182706,0.0002086713,0.00008362592,0.0002912657,0.000214191,0.6443963,0.0566352,0.002871871,0.0008113534,0.2821196],"study_design_scores_gemma":[0.000002568391,0.00001177871,0.0008940828,0.000001979895,0.00000363623,0.00000993537,0.00001060513,0.9957384,0.003043582,0.0002125062,0.00006732183,0.000003654708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4505524,0.0002249868,0.546061,0.0001055878,0.00004838012,0.00004241507,0.0001152829,0.0004163793,0.002433541],"genre_scores_gemma":[0.9551784,0.0001104423,0.0434414,0.00001389881,0.00001318729,0.00003400554,0.0001111275,0.00001934029,0.001078156],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003723905,"threshold_uncertainty_score":0.007404506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01301901089244553,"score_gpt":0.2277482068803596,"score_spread":0.2147291959879141,"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."}}