{"id":"W4408379069","doi":"10.1051/e3sconf/202561901005","title":"IOT-Enabled Fault Diagnosis and Monitoring for Small Wind Turbine","year":2025,"lang":"en","type":"article","venue":"E3S Web of Conferences","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Turbine; Fault (geology); Computer science; Reliability engineering; Environmental science; Real-time computing; Marine engineering; Engineering; Geology; Aerospace engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001349796,0.0004010018,0.0003235534,0.0005660526,0.000240809,0.0002877029,0.0003851813,0.0003927169,0.00263974],"category_scores_gemma":[0.0004278412,0.0001262927,0.0001690994,0.0002597079,0.00014143,0.0005945689,0.0002811854,0.0001995696,0.0006357685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001859513,"about_ca_system_score_gemma":0.0001502477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000641146,"about_ca_topic_score_gemma":0.001378097,"domain_scores_codex":[0.9998503,0.00002053755,0.00001081792,0.00003483221,0.00006676361,0.00001677396],"domain_scores_gemma":[0.9997656,0.00006022205,0.0000404904,0.00004472055,0.00007053151,0.00001844828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001336353,0.0003700087,0.01256513,0.0006055181,0.00007058305,0.001639165,0.0002949691,0.03565183,0.2593454,0.00288918,0.0128697,0.6723621],"study_design_scores_gemma":[0.0001159326,0.001195764,0.02958686,0.0001740473,0.0001155411,0.001993594,0.000274089,0.7853658,0.1486185,0.004655201,0.02784665,0.00005807182],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2284041,0.002042179,0.7438516,0.0006492194,0.0004880207,0.0003027642,0.0007008825,0.008751199,0.01481001],"genre_scores_gemma":[0.9519302,0.000331379,0.0431605,0.0001196989,0.00004807943,0.00009054079,0.0002735537,0.00004541766,0.00400064],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00263974,"threshold_uncertainty_score":0.008830786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02023586737624683,"score_gpt":0.287956512058076,"score_spread":0.2677206446818292,"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."}}