{"id":"W4416078238","doi":"10.1109/mce.2025.3588306","title":"Physical Artificial Intelligence in Consumer Electronics","year":2025,"lang":"en","type":"article","venue":"IEEE Consumer Electronics Magazine","topic":"Artificial Intelligence Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Custom Security Industries (Canada); Wilfrid Laurier University; Huawei Technologies (Canada); University of Ottawa","funders":"","keywords":"Electronics; Generative grammar; Perception; Applications of artificial intelligence; Technology forecasting; Ambient 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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005210557,0.0004168303,0.0004867145,0.0005140402,0.0002280397,0.0002314333,0.001844179,0.0001773727,0.00003334427],"category_scores_gemma":[0.0001552038,0.0004544051,0.0001621412,0.002793273,0.0002994467,0.000458025,0.0002387936,0.0009541222,0.001585609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004835922,"about_ca_system_score_gemma":0.001157878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003879648,"about_ca_topic_score_gemma":0.001310561,"domain_scores_codex":[0.9962804,0.0001477347,0.000809071,0.001056906,0.0003915882,0.001314355],"domain_scores_gemma":[0.9975526,0.0004784012,0.0001714915,0.001377942,0.0002779334,0.0001415811],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002722743,0.0004525177,0.0001833769,0.0000137868,0.00005273191,0.00001037409,0.0002008133,0.0004982422,0.03075691,0.7459098,0.001470442,0.2204237],"study_design_scores_gemma":[0.0001484783,0.0001506518,0.0001737531,0.00004721966,0.00005200313,0.00001672522,0.00003082439,0.2340278,0.2639512,0.4393647,0.0613001,0.0007366187],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0617715,0.004793434,0.9223996,0.00404125,0.0005736027,0.0009145179,0.000006817862,0.0004535442,0.00504576],"genre_scores_gemma":[0.9930979,0.0008240477,0.00419198,0.0007634367,0.00007811106,0.0002488555,0.000009266219,0.00003200514,0.000754393],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9313264,"threshold_uncertainty_score":0.9997908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02112004914974261,"score_gpt":0.3096860055122052,"score_spread":0.2885659563624626,"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."}}