{"id":"W1919281677","doi":"10.1109/usnc-ursi.2015.7303487","title":"Near-field coupled RFID tag for carbon dioxide concentration sensing","year":2015,"lang":"en","type":"article","venue":"","topic":"RFID technology advancements","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Chemistry; Algorithm","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":[0.00007196404,0.00008739287,0.0001018614,0.00001815626,0.00002361789,0.00001318613,0.00005488015,0.00009385776,0.000006579816],"category_scores_gemma":[0.00009701402,0.0000916308,0.00001683912,0.0000720637,0.00001850125,0.0000811053,0.00001189412,0.00007513899,0.000008448555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007419542,"about_ca_system_score_gemma":0.00001586636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002765177,"about_ca_topic_score_gemma":0.00003096057,"domain_scores_codex":[0.9994901,0.000003793093,0.0001298044,0.0001051729,0.00007652012,0.0001946182],"domain_scores_gemma":[0.9996938,0.00003901848,0.00001668058,0.0001541426,0.00004907864,0.00004731059],"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":[0.0001822403,0.00005977135,0.01216445,0.0001548476,0.0003077773,0.0000366975,0.0008954882,0.1551603,0.7747071,0.008523377,0.01119403,0.03661392],"study_design_scores_gemma":[0.0007590555,0.00005456997,0.00005404422,0.000009181726,0.00001048872,0.000003730173,0.00007443041,0.7209343,0.272203,0.0009398001,0.004819175,0.0001382072],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8196711,0.0001156203,0.1718111,0.0002255342,0.0005575742,0.0003082436,0.000001164899,0.0008295851,0.006480035],"genre_scores_gemma":[0.9809286,0.000006322653,0.01874269,0.00008648297,0.00004176022,0.000009776357,0.000006723871,0.00002040992,0.0001572284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5657739,"threshold_uncertainty_score":0.3736596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01453722828522932,"score_gpt":0.2382216093708706,"score_spread":0.2236843810856413,"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."}}