{"id":"W7093343700","doi":"10.1016/j.rsase.2025.101770","title":"Remote sensing-based rice mapping in Brazil: Identifying the best approach for segmenting different spectral compositions using deep learning","year":2025,"lang":"en","type":"article","venue":"Remote Sensing Applications Society and Environment","topic":"History of Computing Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Deep learning; Segmentation; Robustness (evolution); Hyperspectral imaging; Field (mathematics); Convergence (economics); Data modeling; Focus (optics); Market segmentation","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008225062,0.001031579,0.0005243229,0.001125397,0.000360533,0.001101848,0.0008223566,0.0006047923,0.0004525912],"category_scores_gemma":[0.001944667,0.000313821,0.0007648866,0.0009644401,0.000409246,0.001235843,0.0007795321,0.00040536,0.00021411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001159435,"about_ca_system_score_gemma":0.001969617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09823397,"about_ca_topic_score_gemma":0.1243619,"domain_scores_codex":[0.9997252,0.0000543485,0.00001429319,0.00009678002,0.00003372063,0.00007565213],"domain_scores_gemma":[0.999687,0.0001121425,0.00004071357,0.00003720017,0.0000906706,0.00003224649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004764497,0.000283972,0.1404544,0.0007265409,0.0004293348,0.0008594585,0.0009835638,0.4622727,0.04645228,0.005263173,0.004090542,0.3377077],"study_design_scores_gemma":[0.00003591885,0.00006337016,0.03742505,0.000124983,0.000133186,0.0001092734,0.001030722,0.9421628,0.01236292,0.00330338,0.003200897,0.00004754854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8698733,0.001850054,0.1159715,0.001683474,0.00005625173,0.00009639202,0.001471701,0.001479803,0.007517446],"genre_scores_gemma":[0.9647059,0.0003905024,0.03299193,0.00008448793,0.00000843964,0.00001851491,0.001006851,0.00006945136,0.0007239452],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09823397,"threshold_uncertainty_score":0.1953244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02343926852397699,"score_gpt":0.2519633166055112,"score_spread":0.2285240480815343,"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."}}