{"id":"W4409357413","doi":"10.36227/techrxiv.174439272.21224126/v1","title":"Survey on the Application with Lightweight Deep Learning Models for Edge Devices","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Technology and Data Analysis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Enhanced Data Rates for GSM Evolution; Deep learning; Computer science; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0006814576,0.0001786642,0.0002221928,0.0001642635,0.0002566635,0.00015008,0.002126024,0.0002434313,0.000004536893],"category_scores_gemma":[0.0000485628,0.0001013302,0.00007195333,0.0003931172,0.00004161262,0.000126267,0.0009758192,0.0004595286,0.00001589433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002573949,"about_ca_system_score_gemma":0.00007674491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002732697,"about_ca_topic_score_gemma":0.002079099,"domain_scores_codex":[0.9987323,0.0001123767,0.0001725501,0.0006731703,0.000141559,0.0001680408],"domain_scores_gemma":[0.9976955,0.0006142887,0.0001620383,0.001352616,0.0001533775,0.00002214956],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001893435,0.00005641217,0.001874332,0.00004964112,0.0002402321,4.758689e-7,0.0001740388,0.03912301,0.000002363401,0.8962393,0.001682643,0.06053863],"study_design_scores_gemma":[0.00006605691,0.00003262863,0.001556604,0.00003168831,0.00004049371,2.318247e-7,0.00001372795,0.9655635,0.0003265603,0.02752893,0.004664354,0.0001752515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004745567,0.0001441841,0.9928563,0.002334374,0.0000504771,0.0004138268,0.00002335455,0.0003392617,0.003363711],"genre_scores_gemma":[0.932517,0.00005974303,0.06345999,0.0007635986,0.00003410492,0.000635432,0.0003731657,0.00000946339,0.002147482],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9320425,"threshold_uncertainty_score":0.4132125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03378925238968677,"score_gpt":0.2668732665392519,"score_spread":0.2330840141495651,"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."}}