{"id":"W4402244312","doi":"10.23977/jeeem.2024.070301","title":"Hierarchical Multi-granularity Joint Learning for Well-Overflow Detection","year":2024,"lang":"en","type":"article","venue":"Journal of Electrotechnology Electrical Engineering and Management","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Granularity; Joint (building); Computer science; Artificial intelligence; Machine learning; Data mining; Engineering; Programming language","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":[0.001394084,0.0009943121,0.001264048,0.001043798,0.0004466654,0.0008908423,0.001587527,0.001059391,0.00108253],"category_scores_gemma":[0.002536679,0.0004415597,0.001024175,0.001186791,0.0005904057,0.002073217,0.00155505,0.001412943,0.0004071056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006363838,"about_ca_system_score_gemma":0.0009279927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005781258,"about_ca_topic_score_gemma":0.006212234,"domain_scores_codex":[0.9992622,0.0001278997,0.00004373628,0.0002678845,0.000145867,0.0001524711],"domain_scores_gemma":[0.9990539,0.0003307757,0.0001601102,0.0001346271,0.0002447599,0.00007594762],"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":[0.0003334137,0.0003473274,0.01178009,0.00009500408,0.0001877276,0.0001470156,0.00014452,0.6121233,0.01080477,0.002776084,0.003042664,0.358218],"study_design_scores_gemma":[0.000003317542,0.00002413831,0.0006260825,0.000002481312,0.00001119144,0.00001103563,0.000007729155,0.9973626,0.0007374836,0.001081609,0.0001263519,0.00000604765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06046368,0.0005873746,0.936682,0.0001819582,0.00003924472,0.00004650099,0.000144148,0.001044368,0.0008106944],"genre_scores_gemma":[0.9075004,0.000266177,0.08983193,0.0001772348,0.00007523426,0.00007492337,0.0004525603,0.00005814407,0.001563476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005781258,"threshold_uncertainty_score":0.01149523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006946444814688191,"score_gpt":0.1987054122416469,"score_spread":0.1917589674269587,"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."}}