{"id":"W4220757574","doi":"10.2139/ssrn.4067065","title":"Fault Identification for Photovoltaic Systems Using a Multi-Output Deep Learning Approach","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Photovoltaic System Optimization Techniques","field":"Energy","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Photovoltaic system; Identification (biology); Deep learning; Fault (geology); Computer science; Artificial intelligence; Control engineering; Engineering; Machine learning; Electrical engineering; Geology; Seismology","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.004039842,0.0002648503,0.0003639395,0.0004221497,0.001448673,0.0002026912,0.0005387525,0.0001118785,0.00002205652],"category_scores_gemma":[0.0001843833,0.0002856256,0.0002272443,0.0005199055,0.0000315284,0.0002983532,0.0001012702,0.001734421,0.000005884161],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003966413,"about_ca_system_score_gemma":0.0008957939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001340711,"about_ca_topic_score_gemma":0.000143831,"domain_scores_codex":[0.9957327,0.0005661942,0.0008089507,0.0004388402,0.0005857352,0.001867572],"domain_scores_gemma":[0.9984327,0.00007114613,0.0008037752,0.000299185,0.000299561,0.00009362694],"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":[0.0001068436,0.000205993,0.0004350378,0.00005809344,0.0002976918,0.000002296887,0.0007726463,0.9122283,0.06029738,0.02169358,0.00005879129,0.003843372],"study_design_scores_gemma":[0.00126107,0.0002273038,0.0000174629,0.00001750513,0.00009038361,0.001424836,0.01014724,0.9721563,0.002042199,0.003132237,0.009092458,0.0003910436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04353864,0.002982907,0.951358,0.00001078459,0.0004629598,0.0009805422,0.00000742399,0.0003451561,0.0003136204],"genre_scores_gemma":[0.987845,0.000296601,0.005816899,0.00002620093,0.0002402273,0.0006563146,0.00008516959,0.0001223154,0.004911264],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9455411,"threshold_uncertainty_score":0.9999596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02445029350298264,"score_gpt":0.265372888083981,"score_spread":0.2409225945809983,"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."}}