{"id":"W2620692280","doi":"10.1017/cjn.2017.160","title":"P.076 Quantitative EEG in Canada: a national technologist survey","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Quantitative electroencephalography; Electroencephalography; Population; Artifact (error); Medicine; Epilepsy; Psychology; Psychiatry; Neuroscience; Environmental health","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.001214017,0.0003118425,0.000310088,0.002769541,0.002330056,0.001599413,0.001262183,0.0004846264,0.006912874],"category_scores_gemma":[0.006156554,0.0003419484,0.0004693267,0.008140976,0.0008756872,0.0006335751,0.001243782,0.0007397403,0.001040138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02722921,"about_ca_system_score_gemma":0.05612523,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9886805,"about_ca_topic_score_gemma":0.9882081,"domain_scores_codex":[0.9969625,0.000122148,0.000212001,0.0002717291,0.0018627,0.0005689585],"domain_scores_gemma":[0.9875388,0.0007995833,0.002222789,0.0001815842,0.00700409,0.002253197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000530311,0.00003961519,0.9590998,0.0002500321,0.0000378606,0.0001570834,0.0009921476,0.00009239549,0.0002040298,0.0002533222,0.02181907,0.01700158],"study_design_scores_gemma":[0.000007771277,0.00002672682,0.9889528,0.0001313587,0.00001802558,0.0001534982,0.002090328,0.0002566496,0.00007878905,0.00004054189,0.008228148,0.00001536503],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8619087,0.004943779,0.0009738406,0.01490581,0.0001244264,0.000455445,0.08302332,0.0002195079,0.03344509],"genre_scores_gemma":[0.9763222,0.004438507,0.0009770015,0.002988695,0.00005103104,0.0001648289,0.01048872,0.00005083171,0.004518199],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02722921,"threshold_uncertainty_score":0.1975627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1036761424953404,"score_gpt":0.320973299338658,"score_spread":0.2172971568433176,"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."}}