{"id":"W3012381221","doi":"10.1016/j.jneumeth.2020.108682","title":"Three dimensional rendering of auditory neuronal responses: A novel illustration of receptive field across frequency, intensity &amp; time domains","year":2020,"lang":"en","type":"article","venue":"Journal of Neuroscience Methods","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Receptive field; Computer science; Speech recognition; Histogram; Stimulus (psychology); Sound intensity; Auditory system; Signal processing; Artificial intelligence; Acoustics; Digital signal processing; Neuroscience; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001615306,0.0001865229,0.0004523944,0.0001399867,0.0001705374,0.00003948596,0.0005306784,0.0000874963,0.00002412696],"category_scores_gemma":[0.01261222,0.0001550501,0.0002299154,0.0007797907,0.0004755366,0.0005410032,0.0002064399,0.0005749848,0.000001910636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003947177,"about_ca_system_score_gemma":0.0002101419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001120025,"about_ca_topic_score_gemma":0.000004990475,"domain_scores_codex":[0.9971626,0.0004520887,0.0008616566,0.0004241427,0.0008174157,0.0002820809],"domain_scores_gemma":[0.9964293,0.001617967,0.001194854,0.0002264249,0.0003448976,0.0001865879],"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":[0.0006576981,0.00009519822,0.0001937503,0.00001773097,0.000002911439,0.00001845278,0.000289397,0.0009399044,0.9957274,0.0001220364,0.0002171438,0.001718416],"study_design_scores_gemma":[0.0008421933,0.003253508,0.03795455,0.0000954003,0.00003358562,0.0005555694,0.00006761351,0.03048117,0.9240394,0.001463074,0.000929076,0.0002848145],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8092809,0.000006616549,0.1870869,0.001886347,0.001541074,0.0001098861,0.00003125555,0.00001336152,0.0000436655],"genre_scores_gemma":[0.9328219,0.00001453513,0.0643556,0.00246291,0.0002570636,0.000001294907,4.485623e-7,0.00001807777,0.00006821861],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.123541,"threshold_uncertainty_score":0.9957049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1453602779910651,"score_gpt":0.378909669309172,"score_spread":0.2335493913181069,"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."}}