{"id":"W4410637473","doi":"10.1016/j.compag.2025.110580","title":"Crop height retrieval from polarimetric SAR data using machine learning: A comparative and validation study","year":2025,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada; Western University","funders":"Agencia Estatal de Investigación; Canadian Space Agency; Natural Sciences and Engineering Research Council of Canada; Ministerio de Ciencia e Innovación; National Natural Science Foundation of China","keywords":"Remote sensing; Polarimetry; Artificial intelligence; Synthetic aperture radar; Environmental science; Computer science; Machine learning; Agricultural engineering; Engineering; Geography","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":[0.004477011,0.0007213297,0.0006444858,0.00151262,0.0004116232,0.0006901852,0.0007887817,0.001011231,0.0007204823],"category_scores_gemma":[0.006592094,0.0002249065,0.0006416652,0.001156338,0.0005947565,0.001318166,0.0005641494,0.0004351734,0.0003819131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004829144,"about_ca_system_score_gemma":0.0004727515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005299152,"about_ca_topic_score_gemma":0.003711459,"domain_scores_codex":[0.9986635,0.0005282692,0.0001177175,0.0001975263,0.0004032812,0.0000897296],"domain_scores_gemma":[0.9923878,0.004713998,0.0003191634,0.0008186761,0.00168472,0.00007567433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003285507,0.002252921,0.09618677,0.0007634498,0.0009574209,0.000316786,0.0005714521,0.3462041,0.05991821,0.001153148,0.002854137,0.4855362],"study_design_scores_gemma":[0.0001033754,0.00126816,0.07031218,0.00006216321,0.0002986356,0.0002280403,0.0003102822,0.891635,0.03361194,0.0004157033,0.001696562,0.00005804026],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9726878,0.0008585121,0.02408879,0.00006109529,0.00003839831,0.00005064171,0.0003331311,0.0002395625,0.001642121],"genre_scores_gemma":[0.9893436,0.0002728967,0.00879361,0.0000190574,0.000009913698,0.00001644524,0.001013448,0.00002677745,0.0005041897],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005299152,"threshold_uncertainty_score":0.02367699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01967959507543253,"score_gpt":0.2659060752663933,"score_spread":0.2462264801909608,"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."}}