{"id":"W4413468376","doi":"10.1002/sdtp.18204","title":"38‐1: Advance HMI for AI‐Enabled Hardware and Applications","year":2025,"lang":"en","type":"article","venue":"SID Symposium Digest of Technical Papers","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Transparency (behavior); Power consumption; Visualization; Embedded system; Computer hardware; Computer architecture; Human–computer interaction; Power (physics); 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":[0.0002379962,0.0001755594,0.0003429149,0.0001393863,0.000157512,0.00008260853,0.0006840343,0.0001326257,0.00000423919],"category_scores_gemma":[0.000109186,0.0001735334,0.0001297124,0.00048237,0.0001379481,0.0003530055,0.0002772388,0.0001515178,0.00000635795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007715996,"about_ca_system_score_gemma":0.0001210101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001369905,"about_ca_topic_score_gemma":0.0000357482,"domain_scores_codex":[0.9985524,0.00003983275,0.0003935518,0.0005564653,0.0001934059,0.0002642756],"domain_scores_gemma":[0.9983376,0.0005102786,0.000143868,0.000711279,0.0002022685,0.00009476493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000441624,0.0002441769,0.00139379,0.0003738721,0.00006210634,0.000001741214,0.00006644669,0.00004276298,0.8842018,0.09677503,0.001473357,0.01532076],"study_design_scores_gemma":[0.002350689,0.0004051533,0.007994628,0.0005479526,0.00008633107,0.00003972133,0.0001150976,0.0002681396,0.09834637,0.006672822,0.8823014,0.0008717068],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.001866433,0.001239855,0.294375,0.02782107,0.0007206189,0.005415741,0.0001427629,0.00147138,0.6669472],"genre_scores_gemma":[0.9957848,0.00005966337,0.00144262,0.0005746656,0.00004358781,0.0009078683,0.000007723203,0.00001235169,0.001166775],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9939183,"threshold_uncertainty_score":0.7076488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00758418378792098,"score_gpt":0.2637920285954247,"score_spread":0.2562078448075037,"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."}}