{"id":"W4408933702","doi":"10.36227/techrxiv.174320129.94245631/v1","title":"PRACH Preamble Detection as a Multi-Class Classification Problem: A Machine Learning Approach Using SVM","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nokia (Canada)","funders":"","keywords":"Preamble; Support vector machine; Class (philosophy); Artificial intelligence; Computer science; Machine learning; Pattern recognition (psychology); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001687385,0.0007021908,0.0009404487,0.0008939591,0.0003870868,0.001199112,0.0006900714,0.00100442,0.0006907718],"category_scores_gemma":[0.004234556,0.0001758549,0.0004139229,0.0007564397,0.0003803291,0.001318727,0.0005083668,0.001147115,0.0002723971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005226095,"about_ca_system_score_gemma":0.0005440678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001367192,"about_ca_topic_score_gemma":0.0008621365,"domain_scores_codex":[0.9991266,0.0003213887,0.00005931744,0.0001826374,0.0002295232,0.00008055547],"domain_scores_gemma":[0.9973974,0.001619468,0.0002697808,0.0002022267,0.0004454916,0.00006560357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005701571,0.0005207626,0.006692138,0.0001575883,0.0001231231,0.0001616563,0.0001033791,0.3813573,0.01195584,0.00668304,0.003828082,0.5878469],"study_design_scores_gemma":[0.000003605206,0.00004261651,0.0004419322,0.000004315146,0.000005991752,0.00002419881,0.00001200989,0.9966343,0.001185469,0.001403711,0.0002373712,0.000004431633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0714946,0.0005918031,0.9259474,0.0004550337,0.0001078896,0.00005573389,0.00006257068,0.0004216663,0.0008632074],"genre_scores_gemma":[0.7904883,0.0003896284,0.2069203,0.0001419909,0.0001978181,0.00007107764,0.0001668083,0.00004368347,0.001580511],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001687385,"threshold_uncertainty_score":0.008923888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07256493185181481,"score_gpt":0.3250047280552575,"score_spread":0.2524397962034427,"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."}}