{"id":"W2049047741","doi":"10.1109/tbcas.2013.2282087","title":"A DSP for Sensing the Bladder Volume Through Afferent Neural Pathways","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Circuits and Systems","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Digital signal processing; Computer science; Spike sorting; Decoding methods; Signal processing; Noise (video); Digital signal processor; Neurophysiology; Artificial intelligence; Sorting; Computer hardware; Neuroscience; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001332827,0.00019648,0.0002100663,0.0000779911,0.0005429409,0.0002405721,0.00019825,0.00008800591,0.00002484533],"category_scores_gemma":[0.00003664461,0.0001216869,0.00009959599,0.000307673,0.0002806276,0.0002620323,0.00000239937,0.0002548892,0.00003642116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000022305,"about_ca_system_score_gemma":0.00002119569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005993747,"about_ca_topic_score_gemma":0.000001574494,"domain_scores_codex":[0.9983273,0.00008787488,0.0002920982,0.0004587673,0.0003979559,0.0004360681],"domain_scores_gemma":[0.9991041,0.0003975867,0.00005545492,0.0002361676,0.00002925774,0.0001773876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006014772,0.00009171272,0.000001244839,0.0000695529,0.00000787629,0.00001210193,0.0005282852,0.000503687,0.9566705,0.0003823482,0.0006424464,0.04108418],"study_design_scores_gemma":[0.001284864,0.0009271495,0.0001524549,0.0001369423,0.00005609794,0.0006490573,0.0006367728,0.8130999,0.1645308,0.0002911483,0.01756935,0.0006654972],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5454532,0.00006558641,0.4465265,0.001744741,0.004428123,0.001333333,0.00006987214,0.0002060896,0.0001725787],"genre_scores_gemma":[0.9983481,0.00003917564,0.000009709041,0.0008777443,0.0001527308,0.0001014705,5.48505e-7,0.00002331278,0.0004471976],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8125962,"threshold_uncertainty_score":0.4962247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0671892922862449,"score_gpt":0.2551009607221899,"score_spread":0.187911668435945,"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."}}