{"id":"W1510237560","doi":"10.1007/11740674_4","title":"An Architecture for Developing Context-Aware Systems","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Context (archaeology); Architecture; Process (computing); Adapter (computing); Context model; Component (thermodynamics); Software engineering; Human–computer interaction; Programming language; Artificial intelligence; Operating system","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","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00136956,0.0009099054,0.001098003,0.001319562,0.0005064927,0.001668126,0.00436601,0.0006217941,0.000004580375],"category_scores_gemma":[0.00008799609,0.0008691394,0.0002567948,0.0006527678,0.0005059588,0.001149701,0.0007172616,0.0008737947,0.00003553055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007069781,"about_ca_system_score_gemma":0.001257778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002029762,"about_ca_topic_score_gemma":0.0007346777,"domain_scores_codex":[0.9940948,0.0001121451,0.0009854285,0.002539832,0.001212935,0.001054856],"domain_scores_gemma":[0.9950477,0.001240914,0.0006595474,0.001968905,0.0008369377,0.0002459481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001382907,0.00004275566,0.00004774991,0.0002414679,0.00003268927,0.00007164858,0.000660484,0.02552955,0.0001771654,0.0182587,0.0002280708,0.9546959],"study_design_scores_gemma":[0.001257408,0.0005882718,0.0001165411,0.002559785,0.00002961217,0.0006093093,0.000002288214,0.860085,0.001837233,0.08878551,0.04117136,0.00295767],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00006576006,0.0006047592,0.9912684,0.0008403927,0.003974422,0.001756319,0.00005973962,0.0004995077,0.0009307105],"genre_scores_gemma":[0.7872277,0.00001053745,0.2075947,0.002025751,0.002002989,0.0001940714,0.00007508644,0.0001395857,0.0007296102],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9517382,"threshold_uncertainty_score":0.9993759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03102993613819677,"score_gpt":0.2660329413454258,"score_spread":0.235003005207229,"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."}}