{"id":"W6963127403","doi":"10.17632/y2px5tg92h","title":"University of Ottawa Rolling-element Dataset – Vibration and Acoustic Faults under Constant Load and Speed conditions (UORED-VAFCLS)","year":2023,"lang":"en","type":"dataset","venue":"Mendeley Data","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Spectrogram; Microphone; Raw data; Accelerometer; Fast Fourier transform; Data set; Window (computing); Vibration","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0007752606,0.002885875,0.001532403,0.002945948,0.001304913,0.001742289,0.00343293,0.002146528,0.02487274],"category_scores_gemma":[0.004608404,0.0006119277,0.001306344,0.00438102,0.0006727054,0.000963853,0.001807308,0.001112097,0.05087674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00230757,"about_ca_system_score_gemma":0.003641828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2160984,"about_ca_topic_score_gemma":0.4505171,"domain_scores_codex":[0.9987528,0.0001271402,0.0001147395,0.0003810924,0.0003864756,0.0002376476],"domain_scores_gemma":[0.9975979,0.0003205214,0.00015139,0.0007034721,0.001061339,0.0001653393],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001827884,0.00004799434,0.003886452,0.0009826499,0.00009290795,0.00008774515,0.00006344237,0.001474546,0.0007609386,0.0004438215,0.9835693,0.008407419],"study_design_scores_gemma":[0.0001559379,0.00005265177,0.02213921,0.0004178874,0.00008806133,0.0001469114,0.0002898997,0.002544134,0.002073048,0.001208322,0.9707705,0.0001134478],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005724462,0.0001213055,0.0002119967,0.00004463833,0.00003769824,0.00001379449,0.9974499,0.0009360696,0.000612136],"genre_scores_gemma":[0.001033465,0.00005321935,0.000498377,0.00001420886,0.000005692148,0.00004199291,0.9976994,0.00006692751,0.0005866825],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2160984,"threshold_uncertainty_score":0.4296811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05568583822275059,"score_gpt":0.3048567333983349,"score_spread":0.2491708951755843,"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."}}