{"id":"W4410203068","doi":"10.1016/j.jag.2025.104570","title":"Estimation of sugarcane biomass from Sentinel-2 leaf area index using an improved SAFY model (SAFY-Sugar)","year":2025,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"National Natural Science Foundation of China","keywords":"Sugar; Biomass (ecology); Index (typography); Estimation; Leaf area index; Geography; Forestry; Environmental science; Mathematics; Horticulture; Botany; Agronomy; Biology; Engineering; Computer science; Food science","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":[0.0003704966,0.0006448569,0.0003163072,0.000283599,0.000219234,0.0004053245,0.000683518,0.0003503088,0.0005221947],"category_scores_gemma":[0.0005301114,0.0002403326,0.0004294219,0.0003661134,0.0002132323,0.0006527941,0.000323205,0.0004241665,0.0001449304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007311862,"about_ca_system_score_gemma":0.001099637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04847153,"about_ca_topic_score_gemma":0.05093539,"domain_scores_codex":[0.9999104,0.00001008311,0.000004706884,0.00003305325,0.00002558853,0.00001616934],"domain_scores_gemma":[0.9998623,0.00002773664,0.00001648811,0.00001594319,0.00006673106,0.00001077754],"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":[0.0001259132,0.00006920897,0.01728676,0.00004338519,0.00008438494,0.00005229237,0.00006310558,0.9266142,0.02223908,0.0007262407,0.000776608,0.0319187],"study_design_scores_gemma":[0.000007184066,0.00001186657,0.001848731,9.868096e-7,0.000006837794,0.000003264851,0.000004782663,0.9956665,0.00219306,0.00006216304,0.0001873729,0.000007202369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7599343,0.0001412497,0.2346887,0.00009896034,0.00004245249,0.00006960001,0.001002331,0.002107529,0.001914786],"genre_scores_gemma":[0.9281342,0.00009531108,0.06942672,0.00004815289,0.00001044574,0.00006990786,0.0009805709,0.00009916003,0.001135478],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.04847153,"threshold_uncertainty_score":0.09637874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01441258144863603,"score_gpt":0.2357932487421512,"score_spread":0.2213806672935152,"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."}}