{"id":"W3005993943","doi":"10.5194/amt-13-685-2020","title":"Evaluating different methods for elevation calibration of MAX-DOAS (Multi AXis Differential Optical Absorption Spectroscopy) instruments during the CINDI-2 campaign","year":2020,"lang":"en","type":"article","venue":"Atmospheric measurement techniques","topic":"Atmospheric Ozone and Climate","field":"Earth and Planetary Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; University of Toronto","funders":"Canadian Space Agency; Max-Planck-Gesellschaft; University of Toronto; Universität für Bodenkultur Wien; Austrian Science Fund","keywords":"Differential optical absorption spectroscopy; Elevation (ballistics); Calibration; Remote sensing; Optics; Absorption (acoustics); Environmental science; Physics; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004949705,0.001337067,0.0006846762,0.001238035,0.0008624212,0.001400232,0.00148438,0.0008444645,0.00122953],"category_scores_gemma":[0.004959482,0.0004578599,0.0007816729,0.001658686,0.0003578117,0.001038326,0.001281588,0.0008730553,0.0008378126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001191776,"about_ca_system_score_gemma":0.001059531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01424204,"about_ca_topic_score_gemma":0.0325145,"domain_scores_codex":[0.9971635,0.0006290005,0.0001072296,0.0006542364,0.001127567,0.000318449],"domain_scores_gemma":[0.9979406,0.0005023078,0.0002296297,0.0003811682,0.0008486685,0.0000976105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003029689,0.0007409498,0.3434802,0.00151709,0.001818826,0.0003647856,0.001943249,0.111008,0.1620156,0.002714962,0.008463296,0.3629035],"study_design_scores_gemma":[0.0006168834,0.0008274458,0.5347209,0.0002798024,0.0008761663,0.0002527107,0.0009876264,0.2326316,0.1924262,0.001229994,0.03480988,0.0003408512],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8186742,0.001991276,0.1501961,0.0004892031,0.0006152112,0.0005833522,0.006484038,0.003051775,0.01791491],"genre_scores_gemma":[0.8444214,0.0004115512,0.1448175,0.0001816752,0.0001007507,0.0003904251,0.006353786,0.0007638006,0.002559173],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01424204,"threshold_uncertainty_score":0.02831829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08065831034613914,"score_gpt":0.3301351739154145,"score_spread":0.2494768635692754,"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."}}