{"id":"W2525785449","doi":"10.5194/amt-10-2163-2017","title":"AirCore-HR: a high-resolution column sampling to enhance the vertical description of CH <sub>4</sub> and CO <sub>2</sub>","year":2017,"lang":"en","type":"article","venue":"Atmospheric measurement techniques","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Oceanic and Atmospheric Administration; Centre National d’Etudes Spatiales; EIT Climate-KIC; Bundesministerium für Bildung und Forschung","keywords":"Troposphere; Resolution (logic); High resolution; Stratosphere; Sampling (signal processing); Analytical Chemistry (journal); Environmental science; Meteorology; Chemistry; Atmospheric sciences; Remote sensing; Physics; Geology; Chromatography; Optics; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001072927,0.0003541779,0.0003587221,0.000002765464,0.0007525107,0.0001217556,0.0006030424,0.00017856,0.00003780654],"category_scores_gemma":[0.0001706342,0.000303388,0.00009904162,0.0001566383,0.0007789079,0.0004160464,0.0004844745,0.0002495094,0.0000492624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007671875,"about_ca_system_score_gemma":0.00002441508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008067732,"about_ca_topic_score_gemma":0.0003557253,"domain_scores_codex":[0.9971303,0.0001076867,0.0005079344,0.0006424196,0.001073595,0.0005381061],"domain_scores_gemma":[0.9985396,0.00003545556,0.0002673103,0.0009139739,0.00003498457,0.0002086798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008098671,0.0001432766,0.02439453,0.00002107816,0.0000297225,0.00000264626,0.0001576733,0.00105915,0.8641739,0.00007244317,0.000773603,0.1090909],"study_design_scores_gemma":[0.0002262201,0.0004418091,0.3030314,0.0001340856,0.00008311636,0.00000958315,0.00009392419,0.004242181,0.6894043,0.0007381374,0.001137175,0.0004580428],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7863812,0.00009195775,0.2116911,0.0002938976,0.00009858476,0.0008160903,0.000002512945,0.0001292438,0.0004954448],"genre_scores_gemma":[0.9543713,0.0004363675,0.04455133,0.0003138738,0.00005383958,0.0002065769,0.000003079834,0.00004470907,0.00001893551],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2786369,"threshold_uncertainty_score":0.9999418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02285836890812898,"score_gpt":0.2425746423678122,"score_spread":0.2197162734596832,"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."}}