{"id":"W4410809182","doi":"10.1109/les.2025.3574563","title":"A Memory Representation of Random Forests Optimized for Resource-Limited Embedded Devices","year":2025,"lang":"en","type":"article","venue":"IEEE Embedded Systems Letters","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Mitacs","keywords":"Computer science; Representation (politics); Resource (disambiguation); Random access memory; Embedded system; Real-time computing; Computer architecture; Computer hardware; Computer network","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005418343,0.0002103859,0.0005092355,0.0002405297,0.0001806141,0.0001790004,0.0008649103,0.00009242376,0.000001630623],"category_scores_gemma":[0.00007229303,0.0001907529,0.0002291988,0.0008702092,0.00006569691,0.0002843502,0.00007471663,0.0001147643,0.000004662625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004116913,"about_ca_system_score_gemma":0.00004361952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005710138,"about_ca_topic_score_gemma":0.00001419243,"domain_scores_codex":[0.9978362,0.0002705093,0.0007176151,0.000556117,0.0002828224,0.0003367086],"domain_scores_gemma":[0.9973803,0.001061574,0.0003999973,0.0009335168,0.0001504343,0.00007418096],"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":[0.0005662724,0.000156646,0.0002552644,0.0005939162,0.0003964783,0.00001279709,0.001703656,0.5723134,0.1227335,0.005926424,0.2882072,0.007134451],"study_design_scores_gemma":[0.007025303,0.00005196404,0.0003265086,0.000422006,0.0001021178,0.00001160673,0.0003666031,0.9560798,0.02924568,0.0003788845,0.005556284,0.0004331717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08238281,0.0001838492,0.9107286,0.003003534,0.0009819017,0.001853902,0.00001062659,0.0002000008,0.0006547565],"genre_scores_gemma":[0.9847524,0.000005067994,0.01171568,0.001656695,0.0002402292,0.0008885118,0.00002377306,0.0000206817,0.0006969941],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9023696,"threshold_uncertainty_score":0.7778678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02047628820797066,"score_gpt":0.285641806751836,"score_spread":0.2651655185438654,"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."}}