{"id":"W4293008096","doi":"10.11159/icepr22.166","title":"Atmospheric Correction of Sentinel-2 Satellite Images for Improvement of Vegetation Indices","year":2022,"lang":"en","type":"article","venue":"Proceedings of the World Congress on New Technologies","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Rural Development Administration","keywords":"Environmental science; Normalized Difference Vegetation Index; Vegetation (pathology); Evapotranspiration; Leaf area index; Atmospheric correction; Climate change; Atmosphere (unit); Satellite; Climate model; Remote sensing; Radiative transfer; Photosynthetically active radiation; Atmospheric sciences; Meteorology; Geography; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001795167,0.00009338998,0.0001826584,0.00007463324,0.00011474,0.00001530678,0.0003336208,0.00003027341,0.00001259771],"category_scores_gemma":[0.0001084806,0.00006356761,0.00007780575,0.000493978,0.0001208543,0.00007881138,0.00004812862,0.0001201139,3.482435e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006010538,"about_ca_system_score_gemma":0.00002058809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002202821,"about_ca_topic_score_gemma":0.00006861572,"domain_scores_codex":[0.9992297,0.000004082748,0.000249669,0.0001551925,0.0002325861,0.0001287861],"domain_scores_gemma":[0.9991783,0.00009561487,0.0005454461,0.00009596548,0.00007344474,0.00001118628],"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":[0.0002682587,0.00004179609,0.1657141,0.000198137,0.00005505867,1.387533e-7,0.0001469357,0.001530591,0.01975476,0.0001520192,0.002630813,0.8095074],"study_design_scores_gemma":[0.0006574304,0.0007044633,0.09412494,0.0002212078,0.00007001073,0.000003213757,0.002884479,0.009436776,0.8793591,0.003570146,0.008780027,0.0001882354],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948787,0.000567921,0.00000282536,0.0006618435,0.0007123864,0.0002912475,0.0000102658,0.00009505905,0.002779725],"genre_scores_gemma":[0.997368,0.0001103269,0.000751422,0.00001925464,0.00001255977,0.00000187272,0.000003336839,0.000003497372,0.001729685],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8596043,"threshold_uncertainty_score":0.2592212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009641457084863905,"score_gpt":0.2141397903897933,"score_spread":0.2044983333049294,"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."}}