{"id":"W2944577387","doi":"10.5194/amt-12-2665-2019","title":"Characterization of a commercial lower-cost medium-precision non-dispersive infrared sensor for atmospheric CO <sub>2</sub> monitoring in urban areas","year":2019,"lang":"en","type":"article","venue":"Atmospheric measurement techniques","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"EIT Climate-KIC; Université de Versailles Saint-Quentin-en-Yvelines","keywords":"Calibration; Environmental science; Remote sensing; Atmospheric pressure; Humidity; Accuracy and precision; Meteorology; Physics; Mathematics; Statistics; Geography","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"],"consensus_categories":[],"category_scores_codex":[0.0007572968,0.0004843027,0.0006164552,0.000004194802,0.0001195056,0.00003368108,0.0004894155,0.0002844278,0.00021943],"category_scores_gemma":[0.00006949765,0.0004890271,0.0002012972,0.0005366258,0.0001943062,0.0004764728,0.0002191105,0.000251864,0.00006640345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001355692,"about_ca_system_score_gemma":0.00004366585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001555555,"about_ca_topic_score_gemma":0.0000245696,"domain_scores_codex":[0.9966388,0.000100894,0.0008070304,0.0007202215,0.001116355,0.0006166875],"domain_scores_gemma":[0.9986049,0.00006626752,0.0004897,0.0006131107,0.00005510048,0.0001709417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002403376,0.0002942322,0.3161913,0.00004549941,0.0000267337,0.000003790999,0.0004080744,0.0009728483,0.6516824,0.00000246519,0.0002292992,0.02990305],"study_design_scores_gemma":[0.001402987,0.0008435003,0.5299154,0.000351215,0.0000609585,0.000004732884,0.0003403582,0.00957187,0.4527289,0.00007523509,0.003897098,0.0008077763],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.936406,0.00002739757,0.05926198,0.00004613971,0.0003454021,0.002874346,0.00001099683,0.0001307672,0.0008969297],"genre_scores_gemma":[0.9462091,0.0002067041,0.05264467,0.00011926,0.0001038862,0.0004412004,0.00003584686,0.00009133721,0.0001479886],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2137241,"threshold_uncertainty_score":0.9997562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01192995605138599,"score_gpt":0.2220740732026557,"score_spread":0.2101441171512697,"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."}}