{"id":"W4394042508","doi":"10.5281/zenodo.4139415","title":"BIRD: Big Impulse Response Dataset","year":2020,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université de Sherbrooke","funders":"","keywords":"Impulse (physics); Geography; Environmental science; Computer science; Physics","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":[0.0005481126,0.002981629,0.001130028,0.00128356,0.000618591,0.001002469,0.002636467,0.001964348,0.01943864],"category_scores_gemma":[0.001637375,0.0004659018,0.001250734,0.001424742,0.0004189977,0.0006826545,0.001279454,0.001828548,0.03166137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006899296,"about_ca_system_score_gemma":0.0008610595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009603917,"about_ca_topic_score_gemma":0.02574097,"domain_scores_codex":[0.99941,0.00009202518,0.00003937541,0.0001695939,0.000183881,0.0001052602],"domain_scores_gemma":[0.9994516,0.00009284248,0.00003914221,0.0001796523,0.0001591314,0.00007760802],"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":[0.0003357189,0.0002448455,0.00172939,0.0005722783,0.0001020362,0.0001522876,0.00003111843,0.003438918,0.00278772,0.0006516666,0.9689229,0.02103122],"study_design_scores_gemma":[0.0007168545,0.0005315754,0.02663643,0.000350334,0.000161287,0.001432525,0.0002473765,0.0448499,0.01651838,0.006823197,0.9013771,0.0003549473],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00977191,0.000653559,0.007289702,0.0003348349,0.0004632593,0.0001682504,0.9664446,0.00954798,0.00532575],"genre_scores_gemma":[0.007692283,0.0001359989,0.004209002,0.0001651808,0.00004993804,0.0001941817,0.9843354,0.0003000658,0.002917861],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01943864,"threshold_uncertainty_score":0.06502879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04024886661286022,"score_gpt":0.2661941935537605,"score_spread":0.2259453269409003,"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."}}