{"id":"W2148654413","doi":"10.1109/iscas.1998.698973","title":"A genetic-algorithm-based multiuser detector for multiple-access communications","year":2002,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Detector; Computer science; Computational complexity theory; Viterbi algorithm; Algorithm; Multiuser detection; Genetic algorithm; Decoding methods; Telecommunications; Machine learning","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":[0.0009431554,0.0004698434,0.0008210278,0.0006255717,0.0003931329,0.0007143868,0.001256525,0.001443249,0.0007060095],"category_scores_gemma":[0.002838783,0.0002587131,0.0003743544,0.0008170888,0.0007591282,0.0008864119,0.0005528846,0.00109529,0.0004246633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006813276,"about_ca_system_score_gemma":0.001207196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00128474,"about_ca_topic_score_gemma":0.001642253,"domain_scores_codex":[0.9991956,0.0002327754,0.00002566109,0.000141289,0.0003488491,0.00005579412],"domain_scores_gemma":[0.999029,0.0005212943,0.00009115462,0.00009184064,0.000226371,0.0000403866],"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":[0.0003157286,0.0003218428,0.002624355,0.0001937041,0.0002367841,0.000302273,0.0001089259,0.4667845,0.04258333,0.07289179,0.004700654,0.4089362],"study_design_scores_gemma":[0.00003634304,0.0001072021,0.0002201782,0.000006183937,0.00001654759,0.0001263115,0.000003900584,0.9816124,0.00662178,0.00898558,0.002240394,0.00002314422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005534189,0.0002365041,0.9931718,0.0001411658,0.00004832246,0.00002563542,0.00001924356,0.0002153571,0.0006077645],"genre_scores_gemma":[0.2174,0.0003797743,0.779278,0.0003572831,0.00007024147,0.0001307058,0.0001066903,0.00003380188,0.002243547],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001443249,"threshold_uncertainty_score":0.004987955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1224429135947724,"score_gpt":0.3431484063157603,"score_spread":0.2207054927209879,"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."}}