{"id":"W4377028538","doi":"10.21203/rs.3.rs-2857096/v1","title":"Fault diagnosis method for unbalance data based on Gramian angular field","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Science and Technology Department of Gansu Province; National Natural Science Foundation of China","keywords":"Gramian matrix; Computer science; Fault (geology); Residual; Data mining; Context (archaeology); Field (mathematics); Artificial neural network; Artificial intelligence; Adversarial system; Pattern recognition (psychology); Algorithm; Mathematics; Eigenvalues and eigenvectors","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003901572,0.0004862011,0.0006198496,0.0008642279,0.0001688938,0.0002803405,0.002782143,0.0007329923,0.0001242101],"category_scores_gemma":[0.005544588,0.0004988613,0.0002313744,0.0006383343,0.00004907454,0.0001022887,0.001740771,0.002495216,0.00008503329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003129769,"about_ca_system_score_gemma":0.0001470047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001006193,"about_ca_topic_score_gemma":0.0004400445,"domain_scores_codex":[0.9955794,0.0004520353,0.0004546685,0.00126806,0.001221449,0.001024407],"domain_scores_gemma":[0.9882525,0.00707633,0.00005782213,0.004037801,0.0003322401,0.0002432756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005152708,0.0002096892,0.005560409,0.007123489,0.0001464971,0.00008896461,0.00008254268,0.05108309,0.00009664559,0.0004080211,0.871846,0.06330311],"study_design_scores_gemma":[0.0003294158,0.0003097384,0.001752414,0.002293901,0.00002911796,4.836572e-7,0.00002550181,0.8760684,0.01216558,0.004136556,0.1022834,0.0006056014],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003989424,0.002296446,0.9267135,0.01587342,0.002025775,0.01467181,0.01960734,0.01060694,0.004215389],"genre_scores_gemma":[0.49927,0.007115953,0.4122747,0.001137664,0.003280256,0.05088108,0.02325447,0.001713536,0.001072321],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8249853,"threshold_uncertainty_score":0.999806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1519110019506329,"score_gpt":0.5010081825842556,"score_spread":0.3490971806336227,"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."}}