{"id":"W4312099992","doi":"10.3390/rs15010065","title":"Land Cover Mapping Using Sentinel-1 Time-Series Data and Machine-Learning Classifiers in Agricultural Sub-Saharan Landscape","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Land cover; Random forest; Synthetic aperture radar; Series (stratigraphy); Computer science; Artificial intelligence; Remote sensing; Cohen's kappa; Machine learning; Land use; Geography; Geology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0008611759,0.0004094666,0.0002908034,0.001398865,0.0001953313,0.0005168444,0.0002437389,0.0003108422,0.0003836744],"category_scores_gemma":[0.001620157,0.0001077804,0.0002915482,0.001456754,0.0001388595,0.0006571249,0.0002272794,0.0001784499,0.0001737147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003151781,"about_ca_system_score_gemma":0.0001850489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009760021,"about_ca_topic_score_gemma":0.009096223,"domain_scores_codex":[0.9996703,0.0001171298,0.00002314216,0.00007912024,0.00005981509,0.00005058666],"domain_scores_gemma":[0.9996568,0.0001241665,0.0000722185,0.00002615456,0.00009830271,0.00002240863],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005100273,0.0003038187,0.4499351,0.0002673225,0.000381629,0.001284496,0.000630706,0.2000636,0.02887767,0.001417954,0.002783429,0.3135444],"study_design_scores_gemma":[0.00002252201,0.0001585563,0.3492302,0.00005702313,0.00008307656,0.0002905603,0.001025957,0.6346999,0.01008737,0.001009273,0.003297116,0.00003844329],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9866323,0.0003196304,0.01107617,0.0001042054,0.00002513582,0.00002071848,0.000548032,0.0001394203,0.0011344],"genre_scores_gemma":[0.980355,0.0002320089,0.0180678,0.00002455594,0.00002193251,0.00002031732,0.001005443,0.00001504171,0.0002579816],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009760021,"threshold_uncertainty_score":0.01940644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0191501161010245,"score_gpt":0.2107065058765281,"score_spread":0.1915563897755037,"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."}}