{"id":"W3134370859","doi":"10.3390/rs13040806","title":"Crop Biomass Mapping Based on Ecosystem Modeling at Regional Scale Using High Resolution Sentinel-2 Data","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Toronto; Natural Resources Canada","funders":"Canadian Space Agency","keywords":"Leaf area index; Environmental science; Primary production; Biomass (ecology); Ecosystem; Growing season; Standing crop; Crop; Remote sensing; Atmospheric sciences; Agronomy; Forestry; Ecology; Geography","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":[0.0004832526,0.0004374332,0.0002065979,0.0003403099,0.0002158592,0.0003654475,0.0004785716,0.0001837655,0.0004508899],"category_scores_gemma":[0.0008144059,0.0001645509,0.000297254,0.0005435821,0.000132179,0.0005003347,0.0002338944,0.0001833804,0.00009849261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001469458,"about_ca_system_score_gemma":0.001545636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3207635,"about_ca_topic_score_gemma":0.4458095,"domain_scores_codex":[0.999899,0.00002686911,0.000004439231,0.00003531043,0.00001761449,0.00001672555],"domain_scores_gemma":[0.9997849,0.00006224521,0.00003161747,0.00003489018,0.00006593305,0.00002049201],"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":[0.0001319706,0.0001440646,0.150888,0.00004810735,0.0002002644,0.00009821971,0.00009189198,0.8138525,0.01148166,0.0006050076,0.0004445113,0.02201381],"study_design_scores_gemma":[0.0000281422,0.00003875159,0.04731717,0.000005802853,0.00003814021,0.00001798358,0.00008042221,0.9506975,0.001139541,0.0002341468,0.0003864718,0.00001581196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903603,0.00005789227,0.007577259,0.0000617058,0.000005408603,0.0000201638,0.0007460161,0.0001575034,0.001013797],"genre_scores_gemma":[0.9855244,0.00005818825,0.01360759,0.00001331012,0.000001977385,0.00001831106,0.0005940097,0.00001269496,0.0001696016],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3207635,"threshold_uncertainty_score":0.6377929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05163401085616061,"score_gpt":0.2449745560658249,"score_spread":0.1933405452096643,"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."}}