{"id":"W4416704893","doi":"10.26868/25222708.2025.1234","title":"Explainable domain adaptation without source data for activity recognition","year":2025,"lang":"","type":"article","venue":"Building Simulation Conference proceedings","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Process (computing); Activity recognition; Transfer of learning; Transparency (behavior); Domain (mathematical analysis); Adaptation (eye); Energy (signal processing); Domain adaptation","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001256498,0.001191686,0.0007816305,0.0008255784,0.0003208588,0.001071714,0.001768085,0.001217516,0.003394884],"category_scores_gemma":[0.006751616,0.0004879245,0.001321872,0.000815011,0.0007057973,0.001877707,0.002886655,0.003223635,0.002061577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006983302,"about_ca_system_score_gemma":0.0008735325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004131947,"about_ca_topic_score_gemma":0.004473474,"domain_scores_codex":[0.9989057,0.0003453226,0.00005112952,0.0004375753,0.0001607988,0.00009955635],"domain_scores_gemma":[0.9973438,0.001265859,0.0001619562,0.000864769,0.0002768363,0.00008685904],"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":[0.0003453182,0.0004230762,0.006370717,0.0003022675,0.00022413,0.0005118421,0.0003647204,0.6262302,0.01277894,0.01591778,0.01088293,0.325648],"study_design_scores_gemma":[0.00001586341,0.00003254638,0.0008069482,0.0000198277,0.00001409373,0.00005037888,0.00003821116,0.9796557,0.002082386,0.01356035,0.003708186,0.0000154665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03471792,0.0006548305,0.9546289,0.0005512281,0.0001788117,0.000127345,0.001324147,0.004835704,0.002981009],"genre_scores_gemma":[0.759726,0.0006797593,0.2234966,0.0006401216,0.0001964258,0.0005244014,0.008043782,0.0005946038,0.0060983],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004131947,"threshold_uncertainty_score":0.01135695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1433214221211334,"score_gpt":0.3512055922935967,"score_spread":0.2078841701724633,"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."}}