{"id":"W2935876239","doi":"10.3390/rs11080920","title":"A Random Forest Machine Learning Approach for the Retrieval of Leaf Chlorophyll Content in Wheat","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":221,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"King Abdullah University of Science and Technology","keywords":"Remote sensing; Spectroradiometer; Environmental science; Hyperspectral imaging; Random forest; Vegetation (pathology); Computer science; Soil science; Reflectivity; Geography; Machine learning","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":[0.001786171,0.0008954445,0.0009144599,0.001271614,0.0005095893,0.0005805942,0.001035301,0.001283485,0.000978022],"category_scores_gemma":[0.002402416,0.0004157226,0.001084433,0.0009950271,0.0003340954,0.0005935729,0.0003815579,0.0009570545,0.0005202316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000606699,"about_ca_system_score_gemma":0.0008674308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01225346,"about_ca_topic_score_gemma":0.01366382,"domain_scores_codex":[0.9996281,0.0001221509,0.00002608595,0.0001109524,0.00005890581,0.0000537892],"domain_scores_gemma":[0.9992303,0.0004734476,0.00005565193,0.00003402907,0.0001834132,0.00002314728],"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":[0.0001909933,0.0001902196,0.003539468,0.000110183,0.0001161588,0.0001525643,0.00006033207,0.6535335,0.00594023,0.001382449,0.002131337,0.3326524],"study_design_scores_gemma":[0.000005220802,0.00002211336,0.0003710799,0.000003786132,0.000006039729,0.00001115107,0.00000526568,0.9986196,0.0003813602,0.0004101158,0.0001596814,0.000004720913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05829659,0.0009524374,0.9376415,0.0002660318,0.00007333148,0.00009709044,0.0002446617,0.001628362,0.0007999809],"genre_scores_gemma":[0.5482534,0.0004771605,0.4467674,0.0002350327,0.0001774673,0.0002901891,0.001198532,0.000109228,0.002491698],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01225346,"threshold_uncertainty_score":0.02436429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01691539378188292,"score_gpt":0.2097543680724292,"score_spread":0.1928389742905463,"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."}}