{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004214112,0.0005132616,0.0003412655,0.0007547271,0.0001445284,0.0003180656,0.0004186195,0.0002376518,0.0005926684],"category_scores_gemma":[0.0005945661,0.0001185904,0.0003982516,0.0004903584,0.0001171747,0.0005022943,0.0004215068,0.0003476418,0.0002246127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002657854,"about_ca_system_score_gemma":0.0003758314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005586064,"about_ca_topic_score_gemma":0.0089823,"domain_scores_codex":[0.9998468,0.00002232986,0.000006445305,0.0000477875,0.00003832042,0.00003822683],"domain_scores_gemma":[0.9998291,0.00004410616,0.0000237792,0.00001967342,0.00006250214,0.00002087919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001024022,0.0004138184,0.05669064,0.0001451442,0.0002070245,0.0005083015,0.0002162387,0.1088839,0.1191579,0.001225383,0.005770342,0.7057574],"study_design_scores_gemma":[0.00001434179,0.0001165711,0.01921827,0.000006379043,0.00005413986,0.0001075183,0.00008011676,0.96354,0.01527153,0.0004866553,0.001087738,0.00001674354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8330626,0.0006630763,0.1602389,0.0001956007,0.0001405796,0.00006614623,0.0005104511,0.001462341,0.003660252],"genre_scores_gemma":[0.9743614,0.0001430277,0.02363427,0.00005722858,0.0000295115,0.00002122069,0.0006548021,0.00002435538,0.001074182],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005586064,"threshold_uncertainty_score":0.01110709,"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."}}