{"id":"W185214625","doi":"10.5006/c2005-05353","title":"Preprocessing Requirements for the Analysis of Electrochemical Noise Data in the Time Domain","year":2005,"lang":"en","type":"article","venue":"","topic":"Scientific Research and Discoveries","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Electrochemical noise; Preprocessor; Noise (video); Computer science; Time domain; Data pre-processing; Electrochemistry; Materials science; Data mining; Artificial intelligence; Electrode; Chemistry; Computer vision","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":[0.001572434,0.0007899058,0.0009377166,0.001716168,0.0008080397,0.002155061,0.001090052,0.0007603061,0.008072302],"category_scores_gemma":[0.01435285,0.0003816196,0.0005262052,0.001834841,0.0004603467,0.001145992,0.0007222858,0.001025628,0.003569596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000497961,"about_ca_system_score_gemma":0.001318469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002380646,"about_ca_topic_score_gemma":0.004196234,"domain_scores_codex":[0.9987093,0.000205159,0.000189245,0.0001701232,0.0006136504,0.000112502],"domain_scores_gemma":[0.9907986,0.004942732,0.0003778247,0.001167652,0.002518887,0.0001943665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001630228,0.0002821021,0.006100216,0.0008654598,0.00006654409,0.001075592,0.0004477926,0.006452326,0.6829192,0.003975972,0.004850345,0.2913342],"study_design_scores_gemma":[0.0001920823,0.001095715,0.06024929,0.000195168,0.0001919019,0.002366628,0.00118634,0.1896769,0.6642861,0.0082502,0.07209952,0.0002100445],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09242783,0.0002805205,0.8915031,0.0006982405,0.0001605644,0.0007047342,0.002345336,0.007810617,0.004069165],"genre_scores_gemma":[0.1760955,0.0004571436,0.8095993,0.0003575486,0.0001429857,0.001472187,0.007045292,0.001131468,0.00369848],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008072302,"threshold_uncertainty_score":0.02700448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04109672211759646,"score_gpt":0.3571573801977635,"score_spread":0.316060658080167,"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."}}