{"id":"W2134913503","doi":"10.1109/jsen.2007.894906","title":"Simultaneous Classification and Concentration Estimation for Electronic Nose","year":2007,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Electronic nose; Mathematical optimization; Computer science; Polynomial; Computation; Convex optimization; Flexibility (engineering); Parametric statistics; Optimization problem; Gradient descent; Estimation theory; Algorithm; Regular polygon; Artificial intelligence; Mathematics; Statistics; Artificial neural network","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":[],"consensus_categories":[],"category_scores_codex":[0.00008953408,0.00009585583,0.00008996549,0.00004469592,0.00007911424,0.00003248685,0.00005197895,0.0001032238,0.000002670139],"category_scores_gemma":[0.0002044326,0.00009448521,0.00002556528,0.00007523314,0.00003949922,0.0001241386,0.000002030736,0.0002387153,0.000003348264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001921515,"about_ca_system_score_gemma":0.000004820071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.165442e-7,"about_ca_topic_score_gemma":0.000001815529,"domain_scores_codex":[0.999306,0.000004544791,0.0002136682,0.00008988768,0.00009162336,0.0002942618],"domain_scores_gemma":[0.9995281,0.0002242565,0.0000589302,0.00006945144,0.00006437379,0.00005491429],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002580477,0.000006125591,0.00002519373,0.00001319402,0.00001276893,0.000005378182,0.000054192,0.217002,0.7410628,0.0003728793,0.0001240509,0.04129566],"study_design_scores_gemma":[0.0003016659,0.00004397044,0.00008783276,0.00001038537,0.00001180454,0.000158277,0.00008252023,0.5243939,0.4703561,0.002958507,0.00148091,0.0001141794],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7392147,0.0001513945,0.2600195,0.00006269466,0.0001326355,0.0001068304,0.000001631214,0.0002077235,0.0001028686],"genre_scores_gemma":[0.9924593,0.0002206918,0.007136871,0.00001275293,0.0001209775,0.000001924992,0.000003180041,0.0000195077,0.00002480613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3073919,"threshold_uncertainty_score":0.3852995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009789267251156846,"score_gpt":0.2554939867272127,"score_spread":0.2457047194760558,"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."}}