{"id":"W2587329666","doi":"10.1109/tsc.2017.2662941","title":"Adaptable Context-Aware Cooking-Safe System","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Context (archaeology); Microcontroller; Ubiquitous computing; Context awareness; Fuzzy logic; Human–computer interaction; Embedded system; Computer security; 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":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005942148,0.0003995,0.0005373847,0.0002484291,0.00270692,0.001574771,0.002430411,0.000181312,0.00001784045],"category_scores_gemma":[0.000003816346,0.0004228496,0.0002459419,0.0002508519,0.00006907578,0.00177265,0.00003866684,0.0004681327,0.0003731302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002098817,"about_ca_system_score_gemma":0.0001002038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001587473,"about_ca_topic_score_gemma":0.001258427,"domain_scores_codex":[0.9971142,0.0001986669,0.0006003083,0.0008938891,0.0005843758,0.0006085294],"domain_scores_gemma":[0.9964696,0.0003529799,0.0006488681,0.001955999,0.0003422145,0.0002303427],"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":[0.00008408314,0.0004755975,0.0006959577,0.001084195,0.0004527717,0.0001767802,0.004491399,0.01415969,0.001230445,0.002616079,0.0001626209,0.9743704],"study_design_scores_gemma":[0.001538757,0.0001470945,0.0009005504,0.001570599,0.00005104746,0.0002415933,0.001677364,0.9786414,0.01079967,0.00004822902,0.003578444,0.000805239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03350174,0.00004589392,0.9566653,0.0004001809,0.003589453,0.0004375876,0.00002379869,0.0009656592,0.004370323],"genre_scores_gemma":[0.9973749,0.00000374004,0.001565558,0.0004028803,0.0001944719,0.00002905595,0.000001836721,0.00003947707,0.0003880634],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9735652,"threshold_uncertainty_score":0.9998223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02767501729170752,"score_gpt":0.2565122919103955,"score_spread":0.228837274618688,"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."}}