{"id":"W3200260935","doi":"10.1016/j.patcog.2021.108332","title":"Gaussian-guided feature alignment for unsupervised cross-subject adaptation","year":2021,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Metric (unit); Artificial intelligence; Computer science; Pattern recognition (psychology); Feature (linguistics); Gaussian; Generalization; Inertial measurement unit; Feature vector; Divergence (linguistics); Performance metric; Mathematics; Engineering","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"],"consensus_categories":[],"category_scores_codex":[0.000438778,0.0002501273,0.0002829443,0.0001347304,0.0002235336,0.0005762295,0.0003147603,0.000234472,0.0001427287],"category_scores_gemma":[0.0001607736,0.0002698341,0.0002199893,0.0003703154,0.00002428509,0.0009859171,0.0001076021,0.0002083686,0.000346118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001470799,"about_ca_system_score_gemma":0.0001451898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004998529,"about_ca_topic_score_gemma":0.0001409003,"domain_scores_codex":[0.9977847,0.0002413316,0.0004055201,0.0007647546,0.0004200448,0.0003836912],"domain_scores_gemma":[0.9981325,0.0003120865,0.0002175718,0.0005004131,0.0007059321,0.0001314997],"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":[0.00002055575,0.000172208,0.001955318,0.0001455684,0.00008167416,0.0000403304,0.0009936832,0.0000148471,0.006459687,0.00003787321,0.002935089,0.9871432],"study_design_scores_gemma":[0.01811781,0.0009448018,0.06753755,0.00182552,0.0002919136,0.001690691,0.00195508,0.2413167,0.593869,0.02093886,0.04720914,0.004302947],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07885208,0.0001432347,0.9151881,0.002278718,0.001246252,0.0007479041,0.0001788793,0.0002989763,0.001065844],"genre_scores_gemma":[0.9843396,0.00002580585,0.01170858,0.001673159,0.000364385,0.0004866407,0.000717293,0.00003459922,0.000649968],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9828402,"threshold_uncertainty_score":0.9999754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.083874387626855,"score_gpt":0.3082893037845101,"score_spread":0.2244149161576551,"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."}}