{"id":"W2122874477","doi":"10.5194/amt-8-1555-2015","title":"Using XCO <sub>2</sub> retrievals for assessing the long-term consistency of NDACC/FTIR data sets","year":2015,"lang":"en","type":"article","venue":"Atmospheric measurement techniques","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Eurostars; Australian Research Council; Natural Sciences and Engineering Research Council of Canada; California Institute of Technology; Université de Liège; Belgian Federal Science Policy Office; Deutsche Forschungsgemeinschaft; Ministry of Business, Innovation and Employment; Antarctica New Zealand; Nova Scotia Research Innovation Trust; Canadian Foundation for Climate and Atmospheric Sciences; European Research Council; Fonds De La Recherche Scientifique - FNRS; Karlsruhe Institute of Technology; Fédération Wallonie-Bruxelles","keywords":"Isotopologue; Consistency (knowledge bases); Environmental science; Remote sensing; Standard deviation; Accuracy and precision; Mathematics; Statistics; Spectral line; Geography; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00270473,0.0003639744,0.0004143995,0.000002276634,0.0002680902,0.00007689263,0.001101256,0.000163549,0.00005501118],"category_scores_gemma":[0.0002263933,0.0002827158,0.0001193225,0.0003721456,0.0006077517,0.0006381912,0.0008325261,0.000187759,0.00001058817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008372727,"about_ca_system_score_gemma":0.0001098518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001499032,"about_ca_topic_score_gemma":0.00003856645,"domain_scores_codex":[0.9966807,0.0001686944,0.0006750481,0.0006671743,0.001304802,0.0005035487],"domain_scores_gemma":[0.9977646,0.00007679198,0.0005114013,0.001393969,0.00006880402,0.0001843986],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001106103,0.0005930288,0.4043902,0.0001348426,0.0002035211,0.00001645566,0.0003751164,0.002741248,0.4026402,0.00007825602,0.005037306,0.1836791],"study_design_scores_gemma":[0.00360178,0.001546304,0.3666069,0.001078416,0.001764291,0.0002217942,0.001716693,0.1137246,0.4844601,0.00482781,0.01657788,0.003873445],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7020188,0.0004109472,0.2950337,0.0001096861,0.0001563044,0.001170937,0.00001314634,0.0001595512,0.0009269465],"genre_scores_gemma":[0.7204139,0.0001059547,0.279098,0.0001874971,0.0000441458,0.00005193246,0.00001854182,0.00005495627,0.00002498385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1798057,"threshold_uncertainty_score":0.9999625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1247768324887877,"score_gpt":0.3204010759720069,"score_spread":0.1956242434832192,"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."}}