{"id":"W3082855802","doi":"10.36227/techrxiv.12896108.v1","title":"Parametric Convolutional Neural Network for Radar-based Human Activity Classification Using Raw ADC Data","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Infineon Technologies (Canada)","funders":"","keywords":"Spectrogram; Artificial intelligence; Convolutional neural network; Computer science; Doppler radar; Radar; Feature (linguistics); Pattern recognition (psychology); Deep learning; Preprocessor; Artificial neural network; Speech recognition; Telecommunications","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002481992,0.0003678432,0.0004168534,0.0001520572,0.0001419533,0.00009605287,0.0008591326,0.0002548727,0.00002024519],"category_scores_gemma":[0.0001452779,0.0004281868,0.0001152359,0.0003078004,0.00007512319,0.0002347137,0.0005581306,0.0007487323,0.000002751734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003662079,"about_ca_system_score_gemma":0.0001090273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005444537,"about_ca_topic_score_gemma":0.0000129267,"domain_scores_codex":[0.9982666,0.0000543738,0.0003522544,0.0007208392,0.0002382058,0.0003677389],"domain_scores_gemma":[0.9980969,0.0002375744,0.0001644894,0.00132029,0.0000860248,0.00009469135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003827396,0.00004334578,0.0004820927,0.0005295405,0.0001009674,0.000002593672,0.000005910244,0.9507347,0.01540815,0.001240857,0.02361689,0.007796656],"study_design_scores_gemma":[0.0002209359,0.00001329727,0.001333258,0.00006067928,0.00006686387,0.000001374211,0.000001852473,0.987053,0.002116511,0.006268119,0.002437723,0.0004263529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006444166,0.0002108862,0.9885647,0.0002882063,0.0005373372,0.001014168,0.0003574964,0.00237916,0.0002038741],"genre_scores_gemma":[0.6101382,0.000006893862,0.3873706,0.00006730762,0.0003954822,0.00006915052,0.001866303,0.00007823331,0.000007816924],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6036941,"threshold_uncertainty_score":0.999817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2535700292247879,"score_gpt":0.3808727689462299,"score_spread":0.127302739721442,"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."}}