{"id":"W7111351982","doi":"10.1016/j.srs.2025.100352","title":"Weed classification in sugarcane fields in Northeast Thailand from multi-temporal Sentinel-1 and Sentinel-2 data together with random forest algorithm","year":2025,"lang":"en","type":"article","venue":"Science of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"North York General Hospital","funders":"Mahasarakham University","keywords":"Random forest; Weed; Vegetation (pathology); Field (mathematics)","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":[0.000318039,0.0004232113,0.0002040813,0.00109871,0.0002504247,0.000364158,0.0003306608,0.0002386429,0.0003027736],"category_scores_gemma":[0.0003251459,0.000100543,0.0003243409,0.001274851,0.0001931248,0.000359152,0.0002423218,0.0001779481,0.00009405392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003648759,"about_ca_system_score_gemma":0.0005759359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06021564,"about_ca_topic_score_gemma":0.0776953,"domain_scores_codex":[0.9998304,0.00001822824,0.00001045645,0.00005350521,0.00004641579,0.00004098746],"domain_scores_gemma":[0.9998471,0.00002906121,0.00002485523,0.00001353676,0.00006858524,0.00001685349],"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":[0.0005728405,0.0004003478,0.4140472,0.0006639184,0.0002845516,0.00155936,0.0007177064,0.2045966,0.09625648,0.000599649,0.005501471,0.2747998],"study_design_scores_gemma":[0.00003140657,0.0001223197,0.3412584,0.00004151182,0.0001362713,0.0002116855,0.001400502,0.6378762,0.01594738,0.0003848516,0.002525913,0.00006346516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941337,0.0001798307,0.003636169,0.00005887328,0.00001072894,0.00001264207,0.001095518,0.0001504096,0.000722176],"genre_scores_gemma":[0.9910953,0.0001573884,0.005514424,0.0000152092,0.000006748122,0.00001365671,0.002740657,0.000009751969,0.0004468291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06021564,"threshold_uncertainty_score":0.1197303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01768139964627684,"score_gpt":0.253389100347071,"score_spread":0.2357077007007941,"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."}}