{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008952833,0.001205833,0.0004986811,0.001028336,0.0005474545,0.001083748,0.001710822,0.001549791,0.1649497],"category_scores_gemma":[0.001108275,0.0005394195,0.000312449,0.0005516549,0.0003881862,0.001836688,0.001672159,0.001875951,0.07244226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008777233,"about_ca_system_score_gemma":0.0009386967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003149133,"about_ca_topic_score_gemma":0.004978076,"domain_scores_codex":[0.9994012,0.00004741782,0.00002453137,0.00007405431,0.0003699447,0.00008285325],"domain_scores_gemma":[0.9993448,0.00006565729,0.00001797934,0.0001079379,0.0003058808,0.0001577237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006823889,0.00024762,0.0007134344,0.0004668597,0.00002566113,0.0003792394,0.0002153357,0.00206926,0.06494886,0.02596951,0.4120853,0.4921966],"study_design_scores_gemma":[0.00009181661,0.0002752632,0.001835408,0.000110521,0.00002080472,0.0005637189,0.00003359645,0.01770048,0.02430009,0.003644914,0.9513644,0.00005897676],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01386548,0.005778312,0.3630139,0.003058033,0.003476192,0.00149254,0.006793403,0.07596155,0.5265605],"genre_scores_gemma":[0.09384211,0.003725057,0.2607093,0.002047372,0.001251834,0.001219895,0.01508178,0.004603294,0.6175193],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1649497,"threshold_uncertainty_score":0.5518117,"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."}}