{"id":"W4224010467","doi":"10.1080/07038992.2022.2056435","title":"A Comparison between Sentinel-2 and Landsat 8 OLI Satellite Images for Soil Salinity Distribution Mapping Using a Deep Learning Convolutional Neural Network","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Normalized Difference Vegetation Index; Convolutional neural network; Soil salinity; Satellite; Remote sensing; Deep learning; Salinity; Satellite imagery; Environmental science; Artificial neural network; Vegetation (pathology); Computer science; Artificial intelligence; Pattern recognition (psychology); Soil science; Geography; Soil water; Geology; Engineering","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":[0.0007598404,0.0001178296,0.0002444728,0.00006491302,0.001110114,0.00008700138,0.00007569071,0.0000351005,0.00002195992],"category_scores_gemma":[0.00015899,0.0001340693,0.00006977817,0.0001998787,0.0001200842,0.00009287044,0.00006415659,0.0003690938,9.099075e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004169828,"about_ca_system_score_gemma":0.0001005623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005209699,"about_ca_topic_score_gemma":0.002448974,"domain_scores_codex":[0.9987208,0.0001417224,0.0003684663,0.0001579507,0.0001951592,0.0004158931],"domain_scores_gemma":[0.9990548,0.0001698831,0.0003499141,0.00005972982,0.00004505411,0.0003205988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003287958,0.000004141464,0.4441993,0.00002820641,0.00007274419,0.0001259689,0.001104851,0.1803496,0.0007815841,0.00001548075,0.001035939,0.3722493],"study_design_scores_gemma":[0.0005007306,0.00005924943,0.1811981,0.00006064866,0.0000835215,0.0004696982,0.0008470257,0.7734479,0.00002532786,0.0007198293,0.04236386,0.0002240417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8068947,0.0005554997,0.1918632,0.00023929,0.0002587486,0.00008623713,0.00003551848,0.000006280865,0.00006054528],"genre_scores_gemma":[0.9747575,0.00001048627,0.0247431,0.00009163248,0.0002891941,4.199364e-8,0.00007702309,0.00001414668,0.00001690873],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5930983,"threshold_uncertainty_score":0.8538212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03039248484520905,"score_gpt":0.255508552685914,"score_spread":0.2251160678407049,"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."}}