{"id":"W4394880444","doi":"10.3390/rs16081414","title":"Methane Retrieval from Hyperspectral Infrared Atmospheric Sounder on FY3D","year":2024,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; State Key Laboratory of Remote Sensing Science; National Natural Science Foundation of China","keywords":"Environmental science; Atmospheric methane; Remote sensing; Methane; Atmospheric Infrared Sounder; Hyperspectral imaging; Atmospheric sciences; Latitude; Satellite; Atmosphere (unit); Meteorology; Troposphere; Geology; Geodesy; Chemistry; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002582954,0.0005275435,0.0003867497,0.0006207976,0.0002683718,0.000286445,0.0003876945,0.0003527946,0.0009834386],"category_scores_gemma":[0.000241059,0.0001841442,0.0004484567,0.0008122366,0.0001225323,0.0005894352,0.0002997503,0.0002785757,0.0003277643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004111669,"about_ca_system_score_gemma":0.0006001422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02031197,"about_ca_topic_score_gemma":0.02780786,"domain_scores_codex":[0.9998597,0.00001163686,0.000004450552,0.00003171878,0.00006248272,0.00003002959],"domain_scores_gemma":[0.9999353,0.000007814087,0.000008978336,0.000007489307,0.00003263862,0.000007798208],"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":[0.0014206,0.0003531127,0.1121579,0.0002683359,0.0002012657,0.0005158798,0.0003935299,0.04644582,0.6377813,0.0007574926,0.006480331,0.1932244],"study_design_scores_gemma":[0.0002383526,0.000215562,0.3095554,0.00003367937,0.0001539466,0.000167096,0.0002649224,0.5600208,0.123497,0.0003863439,0.005377776,0.00008909787],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9879126,0.0002144828,0.007532385,0.00006951793,0.00003776104,0.00001874774,0.001591929,0.0003980889,0.00222438],"genre_scores_gemma":[0.971962,0.0001776957,0.02170871,0.00008274461,0.00003181794,0.00002619932,0.004692693,0.00005637603,0.00126168],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02031197,"threshold_uncertainty_score":0.04038745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009412651149056485,"score_gpt":0.2190263854570371,"score_spread":0.2096137343079807,"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."}}