{"id":"W4404743200","doi":"10.1016/j.jag.2024.104280","title":"Estimating canopy nitrogen content by coupling PROSAIL-PRO with a nitrogen allocation model","year":2024,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Canopy; Nitrogen; Geography; Environmental science; Mathematics; Forestry; Physics","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":[0.0006006171,0.0008345481,0.0005625772,0.0003956329,0.0002880399,0.0005793698,0.0009613219,0.0006963159,0.0006660012],"category_scores_gemma":[0.0005689312,0.0004005305,0.0006657705,0.0002682641,0.0002484602,0.0008980518,0.00069781,0.0005016295,0.0001857204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000500557,"about_ca_system_score_gemma":0.0008045661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00748544,"about_ca_topic_score_gemma":0.009105068,"domain_scores_codex":[0.9998251,0.00002918928,0.0000107019,0.00007679592,0.0000413458,0.00001687596],"domain_scores_gemma":[0.9997661,0.00007402294,0.00003665083,0.00002395647,0.00007830461,0.00002092999],"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":[0.0001403464,0.0001286896,0.01009964,0.000108006,0.0001187828,0.00008662089,0.00005922103,0.9289346,0.02099702,0.001520116,0.0004522153,0.03735475],"study_design_scores_gemma":[0.000003603129,0.00001255291,0.0007123795,0.000001382187,0.000007463429,0.000005717762,0.000003056458,0.998021,0.000931883,0.0001693798,0.0001260207,0.000005687677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3781994,0.000269423,0.6139173,0.0001568234,0.0000748878,0.000127652,0.0004502766,0.00189907,0.004905096],"genre_scores_gemma":[0.9142389,0.00007904715,0.08392123,0.00006891815,0.00002443047,0.00009305561,0.0003948837,0.00008203565,0.001097568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00748544,"threshold_uncertainty_score":0.01488376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01405822148076032,"score_gpt":0.2102711506601233,"score_spread":0.1962129291793629,"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."}}