{"id":"W4385392018","doi":"10.1016/j.rse.2023.113727","title":"A novel semi-empirical model for crop leaf area index retrieval using SAR co- and cross-polarizations","year":2023,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; Natural Resources Canada; University of Toronto","funders":"Canadian Space Agency; National Natural Science Foundation of China; European Space Agency","keywords":"Leaf area index; Remote sensing; Synthetic aperture radar; Environmental science; Vegetation (pathology); Canopy; Backscatter (email); Water content; Growing season; Empirical modelling; Computer science; Geography; Agronomy; Geology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004603075,0.0002978309,0.0003361299,0.00008213579,0.0003563,0.00006101028,0.0001391477,0.0002555316,0.00002253055],"category_scores_gemma":[0.0001693882,0.0002742629,0.0001250188,0.0003315184,0.000590045,0.0001439322,0.0002673462,0.0002179121,0.00003444682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003985901,"about_ca_system_score_gemma":0.00002344498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001560428,"about_ca_topic_score_gemma":0.00001110337,"domain_scores_codex":[0.9977545,0.00004982678,0.0004565621,0.0006506155,0.0005866471,0.0005018371],"domain_scores_gemma":[0.9989923,0.0001501662,0.0002240495,0.0004435607,0.00001449974,0.000175472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006211903,0.00003380311,0.002052593,0.00001836753,0.00002102658,0.000005188666,0.0004660003,0.574755,0.4208512,0.000001588209,0.0002938064,0.001439363],"study_design_scores_gemma":[0.00056582,0.00005147537,0.02752045,0.00005118641,0.0000446248,0.0000689012,0.00004842794,0.958362,0.01170919,0.0002805411,0.0009863751,0.0003110426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7043875,0.00001253268,0.2945527,0.0002318516,0.00006519732,0.00037548,0.0000387355,0.00006293572,0.0002730241],"genre_scores_gemma":[0.8618927,0.00005379578,0.1365065,0.0001751615,0.00006556961,2.43049e-8,0.00005422129,0.00006341549,0.001188586],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.409142,"threshold_uncertainty_score":0.999971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0521720860744669,"score_gpt":0.3026559379684315,"score_spread":0.2504838518939646,"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."}}