{"id":"W4405307808","doi":"10.23977/jnca.2024.090102","title":"ALOHA Improvement Algorithm for Dynamic Frame Time Slots with Transformer","year":2024,"lang":"en","type":"article","venue":"Journal of Network Computing and Applications","topic":"Embedded Systems and FPGA Design","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Aloha; Computer science; Frame (networking); Algorithm; Transformer; Real-time computing; Computer network; Telecommunications; Electrical engineering; Engineering; Wireless; Throughput","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000232563,0.00009356776,0.0001586719,0.00004296877,0.00008549492,0.00007226293,0.00006303567,0.00004077441,0.000002537789],"category_scores_gemma":[5.048392e-7,0.00007121937,0.00005570113,0.000138741,0.00001376291,0.00004757203,0.000003126423,0.0001486302,0.000003330551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002767268,"about_ca_system_score_gemma":0.0000179023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.02583e-7,"about_ca_topic_score_gemma":5.475691e-7,"domain_scores_codex":[0.9994003,0.000005485633,0.0002737472,0.00008447483,0.00008067085,0.0001553163],"domain_scores_gemma":[0.9996764,0.0001048614,0.00004528883,0.00006361064,0.00004747515,0.00006236465],"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":[0.000005331266,0.00001853367,0.000005296218,0.0001637774,0.0002322325,0.00000363393,0.0001927486,0.1312316,0.00144039,0.0007786187,0.003933618,0.8619942],"study_design_scores_gemma":[0.0001909868,0.0001361656,0.00003599649,0.0002308557,0.00005553562,0.0001020717,0.00002756506,0.9596379,0.00003212456,0.0008797576,0.0385675,0.0001035759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004422441,0.002633304,0.9921846,0.00007256677,0.0001279737,0.0002729006,0.000006225205,0.00007448929,0.0002055408],"genre_scores_gemma":[0.9159723,0.0001454843,0.08207475,0.00004105955,0.001507448,0.00003682051,0.000006787722,0.00004977433,0.0001655662],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9115499,"threshold_uncertainty_score":0.2904241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003832838102170427,"score_gpt":0.2217958529825559,"score_spread":0.2179630148803854,"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."}}