{"id":"W4394818977","doi":"10.3390/rs16081376","title":"Early-Season Crop Classification Based on Local Window Attention Transformer with Time-Series RCM and Sentinel-1","year":2024,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Growing season; Synthetic aperture radar; Environmental science; Sliding window protocol; Phenology; Crop; Remote sensing; Agriculture; Computer science; Machine learning; Agronomy; Geography; Forestry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001719324,0.0002307648,0.000157516,0.0000552936,0.0001851696,0.0002016822,0.00005295532,0.0001258277,0.00004345674],"category_scores_gemma":[0.00001221504,0.0001661943,0.00006155483,0.0003623617,0.0002856491,0.0002725125,0.00001125891,0.0002461289,0.0003819386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000209873,"about_ca_system_score_gemma":0.00001428497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000102376,"about_ca_topic_score_gemma":0.00003932167,"domain_scores_codex":[0.9985066,0.00007818315,0.0001744958,0.0005188432,0.0004315872,0.0002903043],"domain_scores_gemma":[0.9995618,0.00004745205,0.00004654445,0.0002239624,0.00001707196,0.000103195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001423895,0.00002203455,0.0004039706,0.00006404585,0.00002652425,0.0001158924,0.0003280776,0.004883008,0.6421086,0.0000135264,0.0009415975,0.3509504],"study_design_scores_gemma":[0.0003081544,0.0001246149,0.07094806,0.0005008827,0.00006662981,0.0001911413,0.00007933901,0.9145355,0.009412946,0.00006678953,0.003419395,0.000346511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9283037,0.00003138,0.05557852,0.002274328,0.0001242173,0.0002737983,0.000001966948,0.0002290003,0.01318315],"genre_scores_gemma":[0.989854,0.00001121669,0.008372005,0.000164603,0.00008847188,1.52667e-8,0.00002149337,0.00003843698,0.001449763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9096525,"threshold_uncertainty_score":0.6777207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00673868751751179,"score_gpt":0.202156267065822,"score_spread":0.1954175795483103,"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."}}