{"id":"W2973650985","doi":"10.1080/07038992.2019.1643707","title":"The Atmospheric Imaging Mission for Northern Regions: AIM-North","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Health Canada; York University; Ministère des Ressources naturelles et des Forêts; Canadian Space Agency; Canadian Forest Service; University of Saskatchewan; Natural Resources Canada; Université de Sherbrooke; University of Toronto; Government of Alberta; Environment and Climate Change Canada; University of Waterloo","funders":"","keywords":"Remote sensing; Environmental science; Geostationary orbit; Satellite; Greenhouse gas; Geostationary Operational Environmental Satellite; Vegetation (pathology); Shortwave; Imaging spectrometer; Air quality index; Radiance; Geography; Meteorology; Spectrometer; Radiative transfer; Geology; Physics; Oceanography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0002303634,0.0001290264,0.0001403551,0.000003940746,0.0004039902,0.0000550316,0.0002156477,0.00003674103,0.00005099337],"category_scores_gemma":[0.0000511245,0.00009436693,0.0001215076,0.0001345633,0.0001756525,0.0001414897,0.00002548486,0.0001671004,0.00004694202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005979627,"about_ca_system_score_gemma":0.0001095255,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.006713854,"about_ca_topic_score_gemma":0.03435786,"domain_scores_codex":[0.9989938,0.00003106361,0.0002660353,0.0001450976,0.0001778666,0.0003860987],"domain_scores_gemma":[0.9990879,0.00006372497,0.0002341082,0.0002144109,0.00001665013,0.0003832345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002243057,0.000002389849,0.1017717,0.000004258774,0.00001670666,0.0000610526,0.0003834191,0.05948183,0.0001847781,0.000005529006,0.0009873519,0.8370786],"study_design_scores_gemma":[0.0005262916,0.00009785705,0.0449177,0.0000949159,0.00004026384,0.0006379141,0.001398102,0.5490215,0.0000229135,0.001151832,0.4017851,0.0003055811],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9336133,0.0003356684,0.06272117,0.001293713,0.0005519796,0.0001901577,7.163101e-7,0.000005768435,0.001287511],"genre_scores_gemma":[0.8818846,0.00007055828,0.1153867,0.0005589355,0.0001092897,2.185835e-8,0.000001170718,0.00004122371,0.00194751],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.836773,"threshold_uncertainty_score":0.9999005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005687261171516337,"score_gpt":0.1862850443229091,"score_spread":0.1805977831513928,"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."}}