{"id":"W4401216158","doi":"10.23977/acss.2024.080502","title":"Estimating LAI and uncertainty in grassland using UAV hyperspectral data and PROSAIL","year":2024,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Grassland; Remote sensing; Environmental science; Mathematics; Geography; Agronomy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003690515,0.000127774,0.0001916515,0.00003434537,0.00005636127,0.0002255786,0.0001075644,0.00004617899,0.000001862195],"category_scores_gemma":[0.000009245769,0.00009148784,0.000007204972,0.0001656601,0.0001030425,0.0005198715,0.0002447496,0.0001267385,0.000001201128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004822182,"about_ca_system_score_gemma":0.000004797877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005007933,"about_ca_topic_score_gemma":0.0003272682,"domain_scores_codex":[0.9989194,0.00007088196,0.0002130948,0.0004773736,0.000133896,0.0001853744],"domain_scores_gemma":[0.9996468,0.0001200818,0.00003825227,0.0001446299,0.000003123403,0.00004709957],"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":[0.00001202212,0.00003576374,0.1461563,0.0006071723,0.00001758416,0.0002540452,0.002618312,0.6783527,0.00568182,0.0001341068,0.0002852101,0.1658449],"study_design_scores_gemma":[0.0001403581,0.00002813527,0.005565032,0.0005698761,0.000004321555,0.0001924765,0.0001024468,0.9920116,0.000007551518,0.0003353283,0.0009080418,0.000134842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9727123,0.01320905,0.01317167,0.00007091974,0.0004217353,0.0002363516,0.00000839165,0.00002594752,0.0001435938],"genre_scores_gemma":[0.9752566,0.0002532504,0.02431252,0.00001583742,0.0001353269,9.55241e-7,0.000005015783,0.000007461408,0.00001308513],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3136589,"threshold_uncertainty_score":0.3730766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02148549161776034,"score_gpt":0.2772128664983153,"score_spread":0.255727374880555,"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."}}