{"id":"W7117319769","doi":"10.1109/igarss55030.2025.11314004","title":"Multi-Frequency SAR to Identify Soil Tillage","year":2025,"lang":"","type":"article","venue":"","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Tillage; Synthetic aperture radar; Coherence (philosophical gambling strategy); Cropping; Conventional tillage; Greenhouse gas; Backscatter (email)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000263506,0.0002937907,0.0001581633,0.0007981505,0.00008730147,0.0002730154,0.0001312645,0.0001631768,0.001326238],"category_scores_gemma":[0.0002444341,0.0001067726,0.0001685701,0.0006982547,0.00006759005,0.000398114,0.0001630643,0.000153252,0.0004113014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001204102,"about_ca_system_score_gemma":0.0001405447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001216718,"about_ca_topic_score_gemma":0.004474171,"domain_scores_codex":[0.9999176,0.00001886367,0.000004538118,0.0000188914,0.00002778281,0.0000122975],"domain_scores_gemma":[0.999762,0.00004442724,0.00006085308,0.00004799609,0.00006931808,0.00001530596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002553884,0.0003030865,0.09158044,0.0003002208,0.0002142768,0.000199389,0.0001656103,0.02666858,0.4863938,0.001644389,0.004246846,0.3880279],"study_design_scores_gemma":[0.00006142176,0.0008237085,0.5248349,0.00008076202,0.000178529,0.000871253,0.0003911418,0.3059162,0.1355134,0.003346081,0.02792069,0.00006183075],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8411839,0.00167527,0.1378861,0.0002564327,0.00009672091,0.0001111997,0.00220555,0.00102162,0.01556304],"genre_scores_gemma":[0.8852351,0.0007925177,0.1082803,0.000121937,0.00004342115,0.00002547955,0.001775415,0.00004941323,0.003676482],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001326238,"threshold_uncertainty_score":0.004436672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01258499043737465,"score_gpt":0.2894038495637958,"score_spread":0.2768188591264211,"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."}}