{"id":"W4384207605","doi":"10.1016/j.energy.2023.128406","title":"Knowledge sharing-based multi-block federated learning for few-shot oil layer identification","year":2023,"lang":"en","type":"article","venue":"Energy","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Generalization; Block (permutation group theory); Identification (biology); Computer science; Layer (electronics); Artificial intelligence; Interference (communication); Class (philosophy); Petroleum; Machine learning; Data mining; Channel (broadcasting); Geology; Mathematics; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001379378,0.001114228,0.002059942,0.001322723,0.001008294,0.001143882,0.00273185,0.002013804,0.003238837],"category_scores_gemma":[0.003327599,0.0004788374,0.001153343,0.001378315,0.000606948,0.002323556,0.002836255,0.001373401,0.001186849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000671162,"about_ca_system_score_gemma":0.002034921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01165725,"about_ca_topic_score_gemma":0.01317873,"domain_scores_codex":[0.9990596,0.0001319499,0.00006554228,0.0003165342,0.000206425,0.0002200642],"domain_scores_gemma":[0.9985354,0.0005606184,0.000113745,0.0003059789,0.0003841197,0.0001000333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008908153,0.0009328174,0.003560051,0.0001436357,0.0002702751,0.0001708774,0.0002135004,0.2292937,0.01765024,0.003234182,0.004586558,0.7390534],"study_design_scores_gemma":[0.00001768174,0.00006975293,0.0004156616,0.000006193126,0.0000289013,0.00003364777,0.0000447036,0.9919198,0.003305549,0.003773412,0.0003730267,0.00001171949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04499537,0.0003454654,0.9508767,0.0001669595,0.00007045185,0.0000685892,0.0002283363,0.002070109,0.001178191],"genre_scores_gemma":[0.7569912,0.0002120434,0.2359352,0.000327945,0.00009463245,0.0001928152,0.001516612,0.0001292506,0.004600142],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01165725,"threshold_uncertainty_score":0.02317876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06030867008475405,"score_gpt":0.288971949616243,"score_spread":0.2286632795314889,"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."}}