{"id":"W4405143047","doi":"10.1016/j.aei.2024.102976","title":"Sequence–spectrogram fusion network for wind turbine diagnosis through few-shot time-series classification","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Spectrogram; Series (stratigraphy); Sequence (biology); Fusion; Shot (pellet); One shot; Turbine; Artificial intelligence; Computer science; Pattern recognition (psychology); Speech recognition; Algorithm; Engineering; Geology; Materials science; Aerospace engineering; Chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001984193,0.000221666,0.000236635,0.00008189572,0.0001607038,0.0003851344,0.0003882041,0.00007569526,0.00001486855],"category_scores_gemma":[0.00005436851,0.0002017994,0.0001376718,0.0007837674,0.00002438404,0.002443978,0.0001081889,0.0001612681,0.00004296887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009431555,"about_ca_system_score_gemma":0.00003137104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001893125,"about_ca_topic_score_gemma":0.000001234244,"domain_scores_codex":[0.9986647,0.000005228615,0.0005071352,0.0001887068,0.0002030334,0.000431183],"domain_scores_gemma":[0.9992121,0.0001515841,0.0001041666,0.0003872039,0.0000787899,0.00006610864],"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":[0.000006776215,0.00001384757,0.00002273404,0.0003227251,0.00007903094,0.000003175634,0.001461242,0.7212282,0.0007858187,0.1241844,0.001091114,0.150801],"study_design_scores_gemma":[0.00008450359,0.0001024099,0.00002982286,0.0001510576,0.00002099084,0.00001181281,0.00005775682,0.8081273,0.0005759055,0.001066311,0.1895536,0.0002184932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00198494,0.000635087,0.9945945,0.0002759317,0.0005535632,0.0003346823,0.00001345942,0.0007252936,0.0008825856],"genre_scores_gemma":[0.05587117,0.0003950301,0.9428362,0.00008112042,0.0002673361,0.0001797494,0.0001026438,0.00003235118,0.0002344524],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1884625,"threshold_uncertainty_score":0.8229139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02061956705807505,"score_gpt":0.2486340427120971,"score_spread":0.2280144756540221,"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."}}